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IEEE Computer Society Magazine Editors in Chief

Computer IEEE Intelligent Systems IEEE Pervasive Computing Jeff Voas, NIST V.S. Subrahmanian, Dartmouth Marc Langheinrich, Università College della Svizzera italiana Computing in Science & Engineering IEEE Internet Computing IEEE Security & Privacy Lorena A. Barba (Interim), George Pallis, University David Nicol, University of Illinois George Washington University of Cyprus at Urbana-Champaign

IEEE Annals of the History IEEE Micro IEEE Software of Computing Lizy Kurian John, University Ipek Ozkaya, Software Gerardo Con Diaz, University of of Texas at Austin Engineering Institute California, Davis IEEE MultiMedia IT Professional IEEE Computer Graphics Shu-Ching Chen, Florida Irena Bojanova, NIST and Applications International University Torsten Möller, Universität Wien

www.computer.org/computingedge 1 JANUARY 2020 • VOLUME 6, NUMBER 1

From Geographic From Dealing With One Collocation Domain at a Time To Worldwide RE Distribution To Dealing With Everywhere Multiple Domains THEME HERE From Wishing for Experienced End Cross- RE With Users Domain RE Everyone To Empowering Newbies

Ubiquitous RE From Focusing on From Wishing for Software Well-Understood To Holistically Taking Processes RE for Open RE Into Consideration Everything To Accepting People, Things, and 27Openness 36Services 41 Automated RE From Direct Interaction With Legend: Cryptocurrencies: UbiquitousRepresentative End Users The IoT Gray Rectangles: Barriers Colored Boxes: Required Transformations To Indirect Interaction With a TransparencyCircles: Dimensions of Ubiquity RequirementsCrowd and Digital versus Privacy Engineering: A Transformation: Paradigm Shift that Toward the Data- Aff ects Everyone Driven Enterprise Blockchain 8 On the Origins and Variations of Blockchain Technologies ALAN T. SHERMAN, FARID JAVANI, HAIBIN ZHANG, AND ENIS GOLASZEWSKI 15 BLOCKCHAIN GEORGE STRAWN

Cryptocurrency 18 A Service-Oriented Perspective on Blockchain Smart Contracts FLORIAN DANIEL AND LUCA GUIDA 27 Cryptocurrencies: Transparency versus Privacy NIR KSHETRI

Digital Transformation 31 Skills and Competencies for Digital Transformation STEPHEN J. ANDRIOLE 36 Ubiquitous Requirements Engineering: A Paradigm Shift that Aff ects Everyone KARINA VILLELA, EDUARD C. GROEN, AND JOERG DOERR

Internet of Things 41 The IoT and Digital Transformation: Toward the Data-Driven Enterprise ALEXANDER A. PFLAUM AND PHILIPP GOLZER 46 Extending Patient-Chatbot Experience with Internet of Things and Background Knowledge: Case Studies with Healthcare Applications AMIT SHETH, HONG YUNG YIP, AND SAEEDEH SHEKARPOUR Departments 4 Magazine Roundup 7 Editor’s Note: Blockchain to the Rescue 46 Conference Calendar 72 Extending Patient-Chatbot Experience with Internet of Things and Background Subscribe to ComputingEdge for free at Knowledge www.computer.org/computingedge. CS FOCUS

Magazine Roundup

of this article from the Septem- ber/October 2019 issue of Com- puting in Science & Engineering eliminate the speed-limiting charge iteration in MD with a novel extended-Lagrangian scheme. The extended-Lagrang- ian reactive MD (XRMD) code drastically improves energy con- he IEEE Computer furry friends. The authors of this servation while substantially Society’s lineup of 12 article from the September 2019 reducing time-to-solution. Fur- T peer-reviewed tech- issue of Computer report on an thermore, the authors introduce nical magazines covers cut- empirical study investigating a new polarizable charge equili- ting-edge topics ranging from the user perceptions of a popu- bration (PQEq) model to accu- software design and computer lar dog activity tracker. Results rately predict atomic charges and graphics to Internet comput- show that these trackers have a polarization. ing and security, from scien- positive impact on owners’ moti- tifi c applications and machine vation to increase their physical IEEE Annals of the intelligence to visualization activities with their dogs. History of Computing and microchip design. Here are highlights from recent issues. Computing in Science & Founding and Growing Engineering Adobe Systems, Inc. Computer Founded in 1982, Adobe Sys- Scalable Reactive tems heralded several of the Log My Dog: Perceived Molecular Dynamics technological innovations nec- Impact of Dog Activity Simulations for essary to precipitate the emer- Tracking Computational Synthesis gence of desktop publishing as The pet industry is catching up Reactive molecular dynamics well as many features of modern in the wearables market, and (MD) simulation is a power- offi ce computing, digital media, pet activity and location track- ful research tool for describing and graphic arts. In this article ers are increasingly worn by our chemical reactions. The authors from the July–September 2019

4 January 2020 Published by the IEEE Computer Society 2469-7087/20 © 2020 IEEE issue of IEEE Annals of the His- to tune two algorithms for char- posing signifi cant complications tory of Computing, Adobe found- acterizing satellite detections of for system and algorithm design. ers Charles Geschke and John wildfi res. In this article from the May/June Warnock cover their professional 2019 issue of IEEE Internet Com- history, the conception of Adobe IEEE Intelligent Systems puting, the authors present a Systems, and its growth. They also dynamic global manager selection explain the technology behind the Using Social Media to Detect algorithm to minimize energy con- advances in computer printing, Socio-Economic Disaster sumption cost by fully exploiting electronic fi le transfer, and digital Recovery the system diversities in geogra- art and photography. Adobe, its There has been growing interest phy and variation over time. The products, and its engineers played in harnessing artifi cial intelligence algorithm makes real-time deci- a key role in these developments, (AI) to improve situational aware- sions based on measurable system which enabled desktop publishing ness for disaster management. parameters through stochastic opti- and the publishing revolution. As a fi rst step toward investigat- mization methods, while achieving the possibility of developing ing performance balance between IEEE Computer Graphics an AI-based method for detecting energy cost and latency. Extensive and Applications socio-economic recovery, this arti- trace-driven simulations verify the cle from the May/June 2019 issue eff ectiveness and effi ciency of the Uncertainty-Aware of IEEE Intelligent Systems stud- proposed algorithm. The authors Visualization for Analyzing ies the correlations between pub- also highlight several potential Heterogeneous Wildfi re lic sentiment on social media and research directions that remain Detections socio-economic recovery activi- open and require future elabora- There is growing interest in using ties as refl ected in market data. tions in analyzing geo-distributed data science techniques to charac- The result shows multiple correla- big data. terize and predict natural disasters tions between sentiment on social and extreme weather events. Such media and the socio-economic IEEE Micro techniques merge noisy data gath- recovery activities involved in ered in the real world, from sources restarting daily routines. Conven- Accelerating Image-Sensor- such as satellite detections, with tional socio-economic recovery Based Deep-Learning algorithms that strongly depend indicators, such as governmental Applications on the noise, resolution, and uncer- statistical data, have a signifi cant In this article from the September/ tainty in these data. In this article time lag before publishing. Using October 2019 issue of IEEE Micro, from the September/October 2019 public sentiment on social media the authors review two inference issue of IEEE Computer Graph- instead can improve situational accelerators that exploit value ics and Applications, the authors awareness in recovery operations. properties in deep neural networks present a visualization approach (DNNs): Diff y and Tactical. Diff y for interpolating multiresolution, IEEE Internet Computing targets spatially correlated acti- uncertain satellite detections of vations in computational imag- wildfi res into intuitive visual rep- Energy-Effi cient Analytics for ing DNNs. Tactical targets sparse resentations. They use extrinsic, Geographically Distributed neural networks using a low-over- intrinsic, coincident, and adja- Big Data head hardware/software weight- cent uncertainty representations Big data analytics on geographi- skipping front-end. The authors as appropriate for understand- cally distributed datasets (across combine these accelerators into Di- ing the information at each stage. data centers or clusters) has been Tactical to boost benefi ts for both To demonstrate their approach, attracting increased interest in scene understanding workloads the authors use their framework both academia and industry, and computational imaging tasks. www.computer.org/computingedge 5 MAGAZINE ROUNDUP

IEEE MultiMedia authors of this article from the attacks show the risks associated April–June 2019 issue of IEEE Per- with these new technologies and A 3D Scene Management vasive Computing describe Eva, a can help us articulate the need for Method Based on the conversational robot developed to better security practices. Triangular Mesh for Large- conduct therapeutic interventions Scale Web3D Scenes for PwD. A previously reported IEEE Software Real-time rendering of large-scale study conducted with Eva using a Web3D scenes was diffi cult to Wizard-of-Oz approach proved that Perceptions of Gender implement in virtual-reality sys- it successfully engaged PwD with Diversity’s Impact on Mood in tems and geographic information the sessions. This article reports Software Development Teams systems (GIS) in the past because improvements to Eva that allow the Gender inequality persists in IT of the technical constraints in CPU, robot to guide the therapy sessions teams. The authors of this article memory, and network bandwidth. without human intervention and from the September/October 2019 In this article from the July–Septem- fi ndings from its deployment in a issue of IEEE Software examine ber 2019 issue of IEEE MultiMedia, a geriatric residence. These improve- how gender composition aff ects model management strategy is pro- ments include the automatic gen- the workplace atmosphere. They posed based on triangular meshes, eration of a therapy script tailored discuss the problem of gender dis- in which neighborhood buildings to the profi le and preferences of the crimination and consider methods are considered as nodes and con- participants, expectations about to reduce inequality. nected. Each node in the mesh has the type and length of responses a set of level-of-detail (LOD) models, by participants to certain queries, IT Professional including high-, medium-, and low- and strategies to recover from com- precision models. Besides a model munication breakdowns. A user Toward a Blockchain-Enabled fi le, the high-precision LOD of the study with fi ve PwD shows that Crowdsourcing Platform node can be a subtriangular mesh. when acting in fully autonomous Crowdsourcing has been pursued The 3D models in a complex scene mode, Eva is as eff ective in engag- as a way to leverage the power can be fl exibly managed with some ing participants in the therapy as of the crowd for many purposes nested triangular meshes. Accord- with the Wizard-of-Oz condition, in diverse sectors, including col- ing to the experimental results, and that communication break- lecting information, aggregating the proposed method eff ectively downs are adequately resolved. funds, and gathering employees. achieves the progressive download- Data integrity and nonrepudiation ing, dynamic loading, and real-time IEEE Security & Privacy are of utmost importance in these display for a large-scale 3D scene. systems and are currently not guar- Its performance is better than the Stealing, Spying, and Abusing: anteed. Blockchain technology has traditional methods. Consequences of Attacks on been proven to improve on these Internet of Things Devices aspects. In this article from the IEEE Pervasive Computing The authors of this article from the September/October 2019 issue of September/October 2019 issue of IT Professional, the authors investi- A Conversational Robot IEEE Security & Privacy studied the gate the benefi ts that the adoption to Conduct Therapeutic security practices of a diverse set of blockchain technology can bring Interventions for Dementia of Internet of Things (IoT) devices in crowdsourcing systems. To this Verbal communication is an essen- with diff erent architectures. They end, they provide examples of real- tial component of eff ective non- found vulnerabilities that can be life crowdsourcing use cases and pharmacological interventions for exploited to launch novel attacks. explore the benefi ts of using block- people with dementia (PwD). The The real-world implications of IoT chain, mainly as a database.

6 ComputingEdge January 2020 EDITOR’S NOTE

Blockchain to the Rescue

any tough problems facing business, Transparency versus Privacy” warns that cyber- government, and individuals could criminals are sometimes able to expose the iden- M be solved through indelible ledgers. tity of cryptocurrency users despite pseudonyms Transparent, secure transaction records could help and concealed IP addresses. improve trust and effi ciency in everything from Business professionals need to understand a payments to voting. Enter blockchain-based sys- variety of new technologies—not just blockchain— tems. This issue of ComputingEdge explores what in order to compete. “Skills and Competencies makes blockchain such a powerful technology with for Digital Transformation,” from IT Professional, the potential to transform numerous industries. provides an overview of the high-tech tools that “On the Origins and Variations of Blockchain companies should consider implementing. IEEE Technologies,” from IEEE Security & Privacy, pro- Software’s “Ubiquitous Requirements Engineering: vides a history of blockchain going back to David A Paradigm Shift that Aff ects Everyone,” describes Chaum’s 1979 vault system. The authors describe the evolving role of software engineering in digi- the foundational elements of the technology and tal transformation, particularly in addressing the compare the properties of diverse blockchain sys- needs of diverse users. tems. IT Professional’s “BLOCKCHAIN” discusses The Internet of Things (IoT) is one of the cru- the technology’s growing popularity with busi- cial technologies that modern businesses need to nesses and other organizations. employ. In “The IoT and Digital Transformation: The fi rst modern blockchain was implemented Toward the Data-Driven Enterprise,” from IEEE Per- in the cryptocurrency bitcoin, and cryptocur- vasive Computing, the authors propose a process rency remains the most common application of for companies that want to adopt IoT solutions. blockchain technology. IEEE Internet Computing’s Healthcare is among the industries that can benefi t “A Service-Oriented Perspective on Blockchain from the IoT, as shown in IEEE Intelligent Systems’ Smart Contracts” examines the underlying tech- “Extending Patient-Chatbot Experience with Inter- nology used in cryptocurrency platforms like Bit- net of Things and Background Knowledge: Case coin and Ethereum. Computer’s “Cryptocurrencies: Studies with Healthcare Applications.”

2469-7087/20 © 2020 IEEE Published by the IEEE Computer Society January 2020 7 REAL-WORLD CRYPTO Editors: Peter Gutmann, [email protected] | David Naccache, [email protected] | Charles C. Palmer, ccpalmer@us.ibm.com

On the Origins and Variations of Blockchain Technologies

Alan T. Sherman, Farid Javani, Haibin Zhang, and Enis Golaszewski | University of Maryland, Baltimore County

e explore the origins of help people understand where and controls who may update state Wblockchain technologies blockchains came from, whether and issue transactions. A private to better understand the enduring they are important, and if they will blockchain is a permissioned block- needs they address. We identify the persist. (For a complete list of refer- chain controlled by one organiza- five key elements of a blockchain, ences, see A. Sherman et al.)1 tion. A consortium blockchain is show the embodiments of these a permissioned blockchain involv- elements, and examine how these Elements of Blockchains ing a group of organizations. In a elements come together to yield Blockchains provide a mechanism permissionless blockchain, anyone important properties in selected through which mutually distrustful may potentially append new blocks, systems. To facilitate comparing the remote parties (nodes) can reach with the consensus policy (e.g., a many variations of blockchains, we consensus on the state of a ledger majority of participants) determin- also describe the four crucial roles of information. To trace the origins ing which continuation is valid. of common blockchain participants. of these technologies, we start by Blockchains achieve consensus Our historical exploration highlights identifying their essential elements and control (and, in particular, the 1979 work of David Chaum, informally. A blockchain is a dis- prevent double spending) in part whose vault system embodies many tributed ledger comprising blocks through applying protocols and of the elements of blockchains. (records) of information, includ- establishing high costs (both eco- ing information about transac- nomic and computational) to modify Understanding tions between two or more parties. the ledger. Typically, permissioned Blockchains The blocks are cryptographically systems run faster than permission- With myriad blockchain distrib- linked to create an immutable led- less systems do because their control uted ledger systems in existence, ger. Nodes may append informa- and consensus strategies depend on more than 550 associated pat- tion to the ledger through invoking faster fault-tolerant protocols3 rather ent applications under review, and transactions. An access policy deter- than on time-consuming crypto- much associated hype, it can be mines who may read the informa- graphic proofs of work (PoWs), and difficult to make sense of these tion. A control policy determines they usually involve fewer nodes. systems, their properties, and how who may participate in the evolu- Gencer et al. show that permission- they compare. Through exploring tion of the blockchain and how new less blockchains (such as Bitcoin the origins of these technologies, blocks may potentially be appended and Ethereum) are much more cen- including David Chaum’s 1979 vault to the blockchain. A consensus policy tralized than many people assume: system, we provide insights and a determines which state of the block- 20 mining pools control 90% of the clear and useful way to think about chain is valid, resolving disputes computing power. blockchains. Our historical perspec- should conflicting possible continu- Some blockchains additionally tive distills important ideas, identi- ations appear. support the idea of smart contracts, fies enduring needs, and shows how As explained by Cachin and which execute terms of agreements changing technologies can satisfy Vukolic,2 a range of control policies between parties, possibly without those needs. This perspective will is possible, including permissioned, human intervention. These agree- consortium, private, and permis- ments might be embodied as arbi-

Digital Object Identifier 10.1109/MSEC.2019.2893730 sionless blockchains. In a permis- trary computer programs including Date of publication: 20 March 2019 sioned blockchain, a body identifies conditional statements.

8 January 2020 Published by the IEEE Computer Society 2469-7087/20 © 2020 IEEE 72 January/February 2019 Copublished by the IEEE Computer and Reliability Societies 1540-7993/19©2019IEEE REAL-WORLD CRYPTO Editors: Peter Gutmann, [email protected] | David Naccache, [email protected] | Charles C. Palmer, [email protected]

Embodiments of PoW for both mining and achiev - and private transaction computa- On the Origins and Variations the Elements ing consensus. tions that protects individual pri- Although the seminal paper on Bit- PoW aims, in part, to defend vacy through physical security. coin appeared in 2008 (with the against Sybil attacks, in which adver- The building blocks of this system of Blockchain Technologies mysterious author Satoshi Naka- saries attempt to forge multiple include physically secure vaults, moto),4 most of the underlying identities and use those forged iden- existing cryptographic primi- technological ideas had arisen many tities to influence the consensus pro- tives (symmetric and asymmetric Alan T. Sherman, Farid Javani, Haibin Zhang, and Enis Golaszewski | University of Maryland, Baltimore County years earlier. A blockchain is a type cess. With PoW, however, a node’s encryption, cryptographic hash of distributed database, an idea that influence on the consensus process functions, and digital signatures), goes back to at least the 1970s (e.g., is proportional to its computational and a new primitive introduced by Wong11). More generally, the idea of power: forging multiple identities Chaum—threshold secret sharing.8 record keeping goes back millennia, that share the adversary’s given com- Chaum’s 1982 work went largely including to ancient Mesopotamia. putational power does not help. To unnoticed, apparently because he Kanare describes proper methods adapt to varying amounts of avail- never made any effort to publish it e explore the origins of help people understand where and controls who may update state for scientific logging, including the able computational resources, PoW in a conference or journal, instead Wblockchain technologies blockchains came from, whether and issue transactions. A private idea of preserving all transaction systems dynamically throttle the pursuing different approaches to to better understand the enduring they are important, and if they will blockchain is a permissioned block- records, in addition to the history difficulty of the PoW problem to achieving individual privacy. needs they address. We identify the persist. (For a complete list of refer- chain controlled by one organiza- of any modifications to the collected achieve a certain target rate at which In Chaum’s system, each vault five key elements of a blockchain, ences, see A. Sherman et al.)1 tion. A consortium blockchain is data—ideas that are found in many the problems are solved. signs, records, and broadcasts each show the embodiments of these a permissioned blockchain involv- systems (e.g., Hyperledger Fabric). Permissioned blockchains can be transaction it processes. Chaum elements, and examine how these Elements of Blockchains ing a group of organizations. In a The idea of immutably chaining modeled using the concept of (Byz- states, “Because the aggregate elements come together to yield Blockchains provide a mechanism permissionless blockchain, anyone blocks of information with a cryp- antine fault-tolerant) state machine in cludes COMPRESSED_HIS- important properties in selected through which mutually distrustful may potentially append new blocks, tographic hash function appears replication, a notion proposed in TORY, the [cryptographic] check- systems. To facilitate comparing the remote parties (nodes) can reach with the consensus policy (e.g., a in the 1979 dissertation of Ralph 1978 by Lamport and, later, con- sum is actually ‘chained’ through the many variations of blockchains, we consensus on the state of a ledger majority of participants) determin- Merkle at Stanford, in which Merkle cisely formalized by Schneider. entire history of consensus states.”9 also describe the four crucial roles of information. To trace the origins ing which continuation is valid. explains how information can be State machine replication specifies He further says, “Nodes remember of common blockchain participants. of these technologies, we start by Blockchains achieve consensus linked in a tree structure now known what are the transactions and in and will provide all messages they Our historical exploration highlights identifying their essential elements and control (and, in particular, as a Merkle hash tree. A linear chain what order they are processed, even have output—each vault saves all it the 1979 work of David Chaum, informally. A blockchain is a dis- prevent double spending) in part is a special case of a tree, and a tree in the presence of (Byzantine) faults has signed, up to some limit, and will whose vault system embodies many tributed ledger comprising blocks through applying protocols and provides a more efficient way of and unreliable communications.3 supply any saved thing on request; of the elements of blockchains. (records) of information, includ- establishing high costs (both eco- chaining information than does a Thereby, to achieve a strong form only dead vaults can cause loss of ing information about transac- nomic and computational) to modify linear chain. Subsequently, in 1990, of transaction consensus, many recently signed things.”9 Understanding tions between two or more parties. the ledger. Typically, permissioned Haber and Stornetta applied these permissioned systems build on the Chaum’s system embodies a Blockchains The blocks are cryptographically systems run faster than permission- ideas to time-stamp documents, cre- ideas from the 1998 Paxos protocol mechanism for achieving member- With myriad blockchain distrib- linked to create an immutable led- less systems do because their control ating the company Surety in 1994. of Lamport7 (which deals only ship consistency: “Among other uted ledger systems in existence, ger. Nodes may append informa- and consensus strategies depend on These prior works, however, do not with crash failures) and from the things, the algorithms must provide a more than 550 associated pat- tion to the ledger through invoking faster fault-tolerant protocols3 rather include other elements and tech- 2002 Practical Byzantine Fault kind of synchronization and agree- ent applications under review, and transactions. An access policy deter- than on time-consuming crypto- niques of blockchain. Tolerance protocol of Castro and ment among nodes about allowing much associated hype, it can be mines who may read the informa- graphic proofs of work (PoWs), and To prevent an adversary from Liskov. Nakamoto observed that new nodes into the network, remov- difficult to make sense of these tion. A control policy determines they usually involve fewer nodes. unduly influencing the consen- the permissionless Bitcoin system ing nodes from the network, and the systems, their properties, and how who may participate in the evolu- Gencer et al. show that permission- sus process, many permissionless realizes Byzantine agreement in status of nodes once in the network.”9 they compare. Through exploring tion of the blockchain and how new less blockchains (such as Bitcoin systems require that new blocks open networks. The system also embodies a weak the origins of these technologies, blocks may potentially be appended and Ethereum) are much more cen- include a proof of computational Arguably, many of the elements form of transaction consensus, albeit including David Chaum’s 1979 vault to the blockchain. A consensus policy tralized than many people assume: work. Nakamoto’s paper cites Back’s5 of blockchains are embodied in vaguely described and apparently system, we provide insights and a determines which state of the block- 20 mining pools control 90% of the 2002 effective construction from David Chaum’s 1979 vault system,8 not supporting concurrent client re - clear and useful way to think about chain is valid, resolving disputes computing power. Hashcash. In 1992, Dwork and described in his 1982 dissertation9 quests: “If the output of one partic- blockchains. Our historical perspec- should conflicting possible continu- Some blockchains additionally Naor proposed proof of compu- at Berkeley, including detailed ular processor module is used as the tive distills important ideas, identi- ations appear. support the idea of smart contracts, tation to combat junk mail. The specifications. Chaum describes output for the entire vault, the other fies enduring needs, and shows how As explained by Cachin and which execute terms of agreements idea and a construction underly- the design of a distributed com- processors must be able to compare changing technologies can satisfy Vukolic,2 a range of control policies between parties, possibly without ing PoW, however, may be seen in puter system that can be estab- their output to its output, and have those needs. This perspective will is possible, including permissioned, human intervention. These agree- an initial form in 1974 in Merkle’s lished, maintained, and trusted by time to stop the output on its way consortium, private, and permis- ments might be embodied as arbi- puzzles,6 which Merkle proposed mutually suspicious groups. It is a through the isolation devices.”9 The

Digital Object Identifier 10.1109/MSEC.2019.2893730 sionless blockchains. In a permis- trary computer programs including to implement public-key cryptog- public record-keeping system with consensus algorithm involves major- Date of publication: 20 March 2019 sioned blockchain, a body identifies conditional statements. raphy. Bitcoin was the first to use group membership consistency ity vote of nodes based on observed

signed messages entering and leav- Chaum assumes, essentially, a unique pseudonym which appears ing vaults. best-effort broadcast model, and he in a roster of acceptable clients.”9 Chaum created his vaults system does not provide mechanisms for To enable private transactions for before the emergence of the terms achieving consensus with unreli- blockchains, engineers are explor- permissioned and permissionless able communications—technolo- ing the application of trusted blockchains, and his system does gies that subsequently have been execution environments, continu- not neatly fall into either of these developed and applied in modern ing an approach fundamental in discrete categories. In Chaum’s permissioned systems. Chaum’s Chaum’s vaults. system, each node identifies itself dissertation does not include the In 1994, Szabo10 coined the uniquely by posting a public key, ideas of PoW, dynamic throttling of term smart contract, but the idea of authenticated by level 2 trustees. work difficulty, and explicit smart systematically applying rules to exe- For this reason, some people may contracts (though Chaum’s vaults cute the terms of an agreement has consider Chaum’s system a permis- support arbitrary distributed pri- a long history in trading systems. sioned blockchain. vate computation). For example, in 1949, with a system This narrow view, however, dimin- Unlike in most blockchain sys- involving ticker tapes and humans ishes the fact that each node can be tems, nodes in Chaum’s system hold applying rules, Future, Inc. gener- authorized in a public ceremony secret values, which necessitates a ated buy and sell orders for com- independently from any trustee. more complex mechanism for restart- modities. Recently, so-called hybrid During this ceremony, vaults are ing after failures. Using what Chaum blockchains have emerged, which assembled from bins of parts, which calls partial keys, any vault can back combine Byzantine fault-tolerant the public (not necessarily nodes) up its state securely by encrypting state machine replication with can inspect and test—a procedure it with a key and then escrowing defenses against Sybil attacks—for that inspired Chaum to coin the this key using what we now call example, PeerCensus, ByzCoin, more limited phrase cut and choose. threshold secret sharing. After reading Solidus, Hybrid Consensus, Elas- Regardless of whether one views Chaum’s February 1979 technical tico, OmniLedger, and RapidChain. some configurations of Chaum’s report8 that describes partial keys, Also, Hyperledger (an umbrella vaults as permissionless systems, Adi Shamir published an elegant project involving Fabric, a system the trust bestowed through the alternate method for secret sharing for permissioned blockchains) and public ceremony creates a system in November 1979. Ethereum (a platform for public whose trust model is the antithesis Chaum also notes that pseudonyms blockchains) have joined forces. of that of a private (permissioned) can play an important role in effect- Recently, researchers have applied blockchain. For these reasons, we ing anonymity: “Another use allows game theory to model and analyze consider Chaum’s system pub- an individual to correspond with a the behaviors of players and mining licly permissioned. record keeping organization under a pools in blockchain-based digital currencies (see Dhamal and Lewen- berg). Table 1 chronicles some of the important cryptographic Table 1. A timeline of selected discoveries in cryptography and blockchain technology. discoveries underlying blockchain 1970 James Ellis, public-key cryptography discovered at Government Communications technologies. For example, in Headquarters (GCHQ) in secret 2018, the European Patent Office issued the first patent on block- 1973 Clifford Cocks, RSA cryptosystem discovered at GCHQ in secret chain—a method for enforcing 1974 Ralph Merkle, cryptographic puzzles (paper published in 1978) smart contracts. 1976 Diffie and Hellman, public-key cryptography discovered at Stanford 1977 Rivest, Shamir, and Adleman, RSA cryptosystem invented at the Massachusetts Comparison of Selected Institute of Technology Blockchain Systems 1979 David Chaum, vaults and secret sharing (dissertation in 1982) To illustrate how the elements come together in actual blockchain systems, 1982 Lamport, Shostak, and Pease, Byzantine Generals Problem we compare a few selected systems, 1992 Dwork and Naor, combating junk mail including Chaum’s vaults, Bitcoin, 2002 Adam Bach, Hashcash Dash, Corda, and Hyperledger Fab- 2008 Satoshi Nakamoto, Bitcoin ric, chosen for diversity. Table 2 describes how each of these sys- 2017 Wright and Savanah, nChain European patent application (issued in 2018) tems carries out the four crucial

10 ComputingEdge January 2020 74 IEEE Security & Privacy January/February 2019 REAL-WORLD CRYPTO

signed messages entering and leav- Chaum assumes, essentially, a unique pseudonym which appears participant roles of any blockchain that implements policy. Despite ledgers, they will likely be around ing vaults. best-effort broadcast model, and he in a roster of acceptable clients.”9 defined ahead. For more context, these significant powers, the control in various forms for a long time. Chaum created his vaults system does not provide mechanisms for To enable private transactions for Table 3 characterizes a few important structure is still more distributed There are, however, some trou- before the emergence of the terms achieving consensus with unreli- blockchains, engineers are explor- properties of these systems and of (anyone can potentially become a bling fundamental conflicts that permissioned and permissionless able communications—technolo- ing the application of trusted one additional system—Ethereum. core developer) than for a permis- have not been solved. These con- blockchains, and his system does gies that subsequently have been execution environments, continu- In his vault system, Chaum9 sioned system controlled entirely flicts include tensions between not neatly fall into either of these developed and applied in modern ing an approach fundamental in identifies four crucial participant by a prespecified entity. In Bitcoin, the following pairs of poten- discrete categories. In Chaum’s permissioned systems. Chaum’s Chaum’s vaults. roles of any blockchain, which in each round, the winning miner (a tially dissonant concerns: privacy system, each node identifies itself dissertation does not include the In 1994, Szabo10 coined the we call watchers, doers, executives, doer) becomes an executive for that and indelibility, anonymity and uniquely by posting a public key, ideas of PoW, dynamic throttling of term smart contract, but the idea of and czars. The watchers passively round. It is instructive to understand accountability, stability and alter- authenticated by level 2 trustees. work difficulty, and explicit smart systematically applying rules to exe- observe and check the state of the how each blockchain system allo- native future continuations, and For this reason, some people may contracts (though Chaum’s vaults cute the terms of an agreement has ledger. The doers (level 1 trustees) cates the four participant roles. current engineering choices and consider Chaum’s system a permis- support arbitrary distributed pri- a long history in trading systems. carry out actions, including serving Table 3 illustrates some of the long-term security. For example, sioned blockchain. vate computation). For example, in 1949, with a system state. The executives (level 2 trust- possible variations of blockchains, recent European privacy laws grant This narrow view, however, dimin- Unlike in most blockchain sys- involving ticker tapes and humans ees) sign (or otherwise attest to) the including varying control and con- individuals the right to demand ishes the fact that each node can be tems, nodes in Chaum’s system hold applying rules, Future, Inc. gener- blocks. The czars (level 3 trustees) sensus policies as well as different that their personal data be erased authorized in a public ceremony secret values, which necessitates a ated buy and sell orders for com- change the executives and their pol- types of smart contracts. Whereas from most repositories (the right independently from any trustee. more complex mechanism for restart- modities. Recently, so-called hybrid icies. Chaum refers to these partici- most blockchain systems maintain to be forgotten). Satisfying this During this ceremony, vaults are ing after failures. Using what Chaum blockchains have emerged, which pants as bodies,9 leaving it unclear a single chain, Corda supports mul- erasure requirement is highly prob- assembled from bins of parts, which calls partial keys, any vault can back combine Byzantine fault-tolerant whether they could be algorithms. tiple independent chains, per node lematic for indelible blockchains, the public (not necessarily nodes) up its state securely by encrypting state machine replication with Although most systems do not or among subsets of nodes. Similarly, especially for ones whose nodes can inspect and test—a procedure it with a key and then escrowing defenses against Sybil attacks—for explicitly specify these roles, all Chaum’s system also supports mul- lack physical security. that inspired Chaum to coin the this key using what we now call example, PeerCensus, ByzCoin, systems embody them, though tiple chains. While most blockchains An attraction of blockchains is more limited phrase cut and choose. threshold secret sharing. After reading Solidus, Hybrid Consensus, Elas- with varying nuances. For example, require each node to maintain the their promise of stability enforced Regardless of whether one views Chaum’s February 1979 technical tico, OmniLedger, and RapidChain. many people naively think of Bit- same state, Corda’s and Chaum’s sys- through consensus, yet sometimes some configurations of Chaum’s report8 that describes partial keys, Also, Hyperledger (an umbrella coin as a fully distributed system tems do not. the nodes cannot agree, resulting in vaults as permissionless systems, Adi Shamir published an elegant project involving Fabric, a system free of any centralized control, but, a fork and associated possible splits the trust bestowed through the alternate method for secret sharing for permissioned blockchains) and in fact, Bitcoin’s core developers— Conflicts and Challenges in the continuations of the chain. In public ceremony creates a system in November 1979. Ethereum (a platform for public as is true for all distributed sys- Because blockchain technologies a hard fork, level 3 trustees issue a whose trust model is the antithesis Chaum also notes that pseudonyms blockchains) have joined forces. tems—carry out the role of czars, address enduring needs for per - significant change in the rules that is of that of a private (permissioned) can play an important role in effect- Recently, researchers have applied changing the underlying software manent, indelible, and trusted incompatible with the old rules. In a blockchain. For these reasons, we ing anonymity: “Another use allows game theory to model and analyze consider Chaum’s system pub- an individual to correspond with a the behaviors of players and mining licly permissioned. record keeping organization under a pools in blockchain-based digital currencies (see Dhamal and Lewen- Table 2. Alignment of participant roles across five blockchain systems. berg). Table 1 chronicles some of the important cryptographic Chaum, 1982 Bitcoin, 2008 Dash, 2014 Corda, 2016 Hyperledger Fabric, Table 1. A timeline of selected discoveries in cryptography and blockchain technology. discoveries underlying blockchain A flexible A permissionless A system that speeds up A permissioned 2016 1970 James Ellis, public-key cryptography discovered at Government Communications technologies. For example, in system based system using Bitcoin with a masternode system with A permissioned system Headquarters (GCHQ) in secret 2018, the European Patent Office Role on vaults PoW network smart contracts with smart contracts issued the first patent on block- 1973 Clifford Cocks, RSA cryptosystem discovered at GCHQ in secret Watchers Any computer Nodes (distinct Any computer online Nodes Peers chain—a method for enforcing Passively check state online9 from full nodes) 1974 Ralph Merkle, cryptographic puzzles (paper published in 1978) smart contracts. Doers Level 1 trustee Full nodes Miners Nodes Peers 1976 Diffie and Hellman, public-key cryptography discovered at Stanford Carry out actions, 1977 Rivest, Shamir, and Adleman, RSA cryptosystem invented at the Massachusetts Comparison of Selected including serving state Institute of Technology Blockchain Systems Executives Level 2 trustee Winning miner Winning masternode Nodes (each Endorsing peers To illustrate how the elements come 1979 David Chaum, vaults and secret sharing (dissertation in 1982) Sign blocks (or (promoted (promoted (promoted by an algorithm node is an together in actual blockchain systems, 1982 Lamport, Shostak, and Pease, Byzantine Generals Problem otherwise attest to from level 1 by from doers from the masternode executive for its we compare a few selected systems, them) czars)9 each round) network, which anyone Corda blocks, 1992 Dwork and Naor, combating junk mail including Chaum’s vaults, Bitcoin, may join for 1,000 Dash) called states) Dash, Corda, and Hyperledger Fab- 2002 Adam Bach, Hashcash Czars Level 3 Core developers Quorum of masternodes Permissioning Endorsement policies 2008 Satoshi Nakamoto, Bitcoin ric, chosen for diversity. Table 2 Change executives and trustee9 service describes how each of these sys- their policies 2017 Wright and Savanah, nChain European patent application (issued in 2018) tems carries out the four crucial

www.computer.org/computingedge 11 74 IEEE Security & Privacy January/February 2019 www.computer.org/security 75 REAL-WORLD CRYPTO

o understand blockchain sys- Table 3. Three properties of several distributed ledger systems. T tems, it is helpful to view them in terms of how the watchers, doers, System Permissioned? Basis of Consensus Smart Contracts executives, and czars carry out their Chaum, Permissioned, Weak consensus; Private arbitrary functions under the guidance of the 1982 with option does not handle distributed access, control, and consensus poli- for publicly concurrent client computation cies. This systematic abstract view permissioned requests helps focus attention on crucial ele - ments and facilitates a balanced Bitcoin, Permissionless PoW Conditional comparison of systems. Blockchains 2008 payment and address many longstanding inherent limited smart needs for indelible ledgers, from finan- contracts through cial transactions to property records scripts and supply chains. With powerful Dash, Combination Proof of stake No existing cryptographic techniques, a 2014 wide set of available variations, and a Ethereum, Permissionless PoW Yes, nonprivate large amount of resources allocated to 2014 Turing complete these technologies, blockchains hold objects significant potential. Hyperledger Permissioned Based on Yes, off-chain Fabric, state machine Acknowledgments 2015 replication We thank Dan Lee, Linda Oliva, and Corda, Permissioned Based on Yes (set of Konstantinos Patsourakos for their 2016 state machine functions), helpful comments. Alan T. Sherman replication including explicit was supported in part by the National links to human Science Foundation under Scholarship language for Service grant 1241576.

References soft fork, there is a less severe change time (Bitcoin’s ledger is currently 1. A. Sherman, F. Javani, H. Zhang, in the rules for which the old system more than 184 GB). and E. Golaszewski, On the ori- recognizes valid blocks created by As of September 2018, the hash gins and variations of blockchain the new system (but not necessar- rate for Bitcoin exceeded 50 mil- technologies. 2018. [Online]. ily vice versa). lion TH/s, consuming more than Available: http://arxiv.org/abs Security engineers must commit 73 TWh of power per day, more /1810.06130 to particular security parameters, than the amount consumed by Swit- 2. C. Cachin and M Vukolic, “Block- hash functions, and digital signa- zerland. These hashes were attempts chain consensus protocols in the tures methods. to solve cryptographic puzzles of wild,” in Proc. 31st Int. Symp. Distrib- No such choice can remain com- no intrinsic value (finding an input uted Computing, 2017, vol. 1, pp. 1–16. putationally secure forever in the that, when hashed, produces a cer- 3. L. Lamport, R. Shostak, and M. face of evolving computer technol- tain number of leading zeroes), and Pease, “The Byzantine generals ogy, including quantum comput- almost all of these computations went problem,” ACM Trans. Program- ers and other technologies not yet unused. Attempts, such as Primecoin ming Languages Syst., vol. 4, no. 3, invented. The hopeful permanence and others, to replace cryptographic pp. 382–401, 1982. [Online]. Avail- of blockchains is dissonant with hash puzzles with useful work (e.g., able: https://dl.acm.org/citation the limited-time security of today’s finding certain types of prime inte- .cfm?doid=357172.357176 engineering choices. gers) are challenging because it is very 4. S. Nakamoto, “Bitcoin: A peer-to- Additional challenges facing hard to find useful problems that have peer electronic cash system,” Bitcoin, block chains include the huge assured difficulty and whose level of 2008. [Online]. Available: https:// amounts of energy spent on block- difficulty can be dynamically throt- bitcoin.org/bitcoin.pdf chain computations (especially tled. Some researchers are exploring 5. A. Back, “Hashcash: A denial of service PoW), the high rates at which ledgers alternatives to PoW, such as proof counter-measure,” Hashcash, 2002. grow, and the associated increases of space, proof of stake, and proof of [Online]. Available: http://www in transaction latency and processing elapsed time. . hashcash.org/papers/hashcash.pdf

12 ComputingEdge January 2020 76 IEEE Security & Privacy January/February 2019 REAL-WORLD CRYPTO This article originally appeared in IEEE Security & Privacy, vol. 17, no. 1, 2019. o understand blockchain sys- 6. R. C. Merkle, “Secure communi- 10. N. Szabo, “Smart contracts,” 1994. [On - Farid Javani is a Ph.D. student at Table 3. Three properties of several distributed ledger systems. T tems, it is helpful to view them cations over insecure channels,” line]. Available: http://www.fon.hum the University of Maryland, Bal- in terms of how the watchers, doers, Commun. ACM, vol. 21, no. 4, .uva.nl/rob/Courses/ Information timore County. Contact him at System Permissioned? Basis of Consensus Smart Contracts executives, and czars carry out their pp. 294–299, 1978. [Online]. Avail- InSpeech/CDROM/Literature [email protected]. Chaum, Permissioned, Weak consensus; Private arbitrary functions under the guidance of the able: https://dl.acm.org/ citation /LOTwinterschool2006/szabo.best 1982 with option does not handle distributed access, control, and consensus poli- .cfm?doid=359460.359473 .vwh.net/smart.contracts.html Haibin Zhang is an assistant profe ssor for publicly concurrent client computation cies. This systematic abstract view 7. L. Lamport, “The part-time parlia- 11. E. Wong, “Retrieving dispersed in the Department of Compu- permissioned requests helps focus attention on crucial ele - ment,” ACM Trans. Comput. Syst., vol. data from SDD-1: A system for ter Science and Electrical Engi- ments and facilitates a balanced 16, no. 2, pp. 133–169, 1998. [Online]. distributed databases,” in Proc. 2nd neering at the Univer sity of Bitcoin, Permissionless PoW Conditional comparison of systems. Blockchains Available: https://dl.acm.org Berkeley Workshop Distributed Data Maryland, Baltimore County. 2008 payment and address many longstanding inherent /citation.cfm?doid=279227.279229 Management and Comput. Networks, Haibin recei ved a Ph.D. from the limited smart needs for indelible ledgers, from finan- 8. D. L. Chaum, “Computer systems May 1977, pp. 217–235. University of Califor nia, Davis, contracts through cial transactions to property records established, maintained, and trusted in 2001. His re search interests scripts and supply chains. With powerful by mutually suspicious groups,” Alan T. Sherman is a professor of include distributed comput- Dash, Combination Proof of stake No existing cryptographic techniques, a Elect. Eng. Res. Lab., Univ. Cali- computer science at the University ing and secure blockchains. 2014 wide set of available variations, and a fornia, Berkeley, Tech. Memo. of Maryland, Baltimore County. Contact him at hbzhang@ Ethereum, Permissionless PoW Yes, nonprivate large amount of resources allocated to UCB/ERL/M79/10, 1979. His research interests include umbc.edu. 2014 Turing complete these technologies, blockchains hold 9. D. L. Chaum, “Computer systems secure voting, applied cryptog- objects significant potential. established, maintained and trusted by raphy, and cybersecurity educa- Enis Golaszewski is a Ph.D. student Hyperledger Permissioned Based on Yes, off-chain mutually suspicious groups,” Ph.D. tion. He is a Senior Member of the at the University of Maryland, Fabric, state machine Acknowledgments dissertation, Dept. Comput. Sci., IEEE. Contact him at sherman@ Baltimore County. Contact him 2015 replication We thank Dan Lee, Linda Oliva, and Univ. California, Berkeley, 1982. umbc.edu. at [email protected]. Corda, Permissioned Based on Yes (set of Konstantinos Patsourakos for their 2016 state machine functions), helpful comments. Alan T. Sherman replication including explicit was supported in part by the National links to human Science Foundation under Scholarship language for Service grant 1241576.

References soft fork, there is a less severe change time (Bitcoin’s ledger is currently 1. A. Sherman, F. Javani, H. Zhang, Call in the rules for which the old system more than 184 GB). and E. Golaszewski, On the ori- recognizes valid blocks created by As of September 2018, the hash gins and variations of blockchain the new system (but not necessar- rate for Bitcoin exceeded 50 mil- technologies. 2018. [Online]. for Articles ily vice versa). lion TH/s, consuming more than Available: http://arxiv.org/abs Security engineers must commit 73 TWh of power per day, more /1810.06130 to particular security parameters, than the amount consumed by Swit- 2. C. Cachin and M Vukolic, “Block- hash functions, and digital signa- zerland. These hashes were attempts chain consensus protocols in the IEEE Pervasive Computing tures methods. to solve cryptographic puzzles of wild,” in Proc. 31st Int. Symp. Distrib- seeks accessible, useful papers on the latest No such choice can remain com- no intrinsic value (finding an input uted Computing, 2017, vol. 1, pp. 1–16. putationally secure forever in the that, when hashed, produces a cer- 3. L. Lamport, R. Shostak, and M. peer-reviewed developments in pervasive, face of evolving computer technol- tain number of leading zeroes), and Pease, “The Byzantine generals mobile, and ubiquitous computing. Topics ogy, including quantum comput- almost all of these computations went problem,” ACM Trans. Program- ers and other technologies not yet unused. Attempts, such as Primecoin ming Languages Syst., vol. 4, no. 3, include hardware technology, software invented. The hopeful permanence and others, to replace cryptographic pp. 382–401, 1982. [Online]. Avail- of blockchains is dissonant with hash puzzles with useful work (e.g., able: https://dl.acm.org/citation infrastructure, real-world sensing and .cfm?doid=357172.357176 the limited-time security of today’s finding certain types of prime inte- Author guidelines: interaction, human-computer interaction, engineering choices. gers) are challenging because it is very 4. S. Nakamoto, “Bitcoin: A peer-to- www.computer.org/mc/ Additional challenges facing hard to find useful problems that have peer electronic cash system,” Bitcoin, and systems considerations, including block chains include the huge assured difficulty and whose level of 2008. [Online]. Available: https:// pervasive/author.htm deployment, scalability, security, and privacy. amounts of energy spent on block- difficulty can be dynamically throt- bitcoin.org/bitcoin.pdf Further details: chain computations (especially tled. Some researchers are exploring 5. A. Back, “Hashcash: A denial of service [email protected] PoW), the high rates at which ledgers alternatives to PoW, such as proof counter-measure,” Hashcash, 2002. grow, and the associated increases of space, proof of stake, and proof of [Online]. Available: http://www www.computer.org/pervasive in transaction latency and processing elapsed time. . hashcash.org/papers/hashcash.pdf Digital Object Identifier 10.1109/MSEC.2019.2900896

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IT and Twenty-First Century Employment

BLOCKCHAIN

George Strawn National Academies of Sciences, Engineering, This article originally appeared in and Medicine IT Professional, vol. 21, no. 1, 2019.

& “YABB,” YET ANOTHER book on blockchain (to become disconnected from the network. (Cold War modify the acronym YACC—yet another complier worries about a nuclear arrack made this a major compiler), Life After Google, was published in July concern.) By switching packets instead of circuits of 2018 by the prolific author George Gilder. (Ama- and by distributing the switching function to every zon currently lists 75 books on the topic.) As Internet router, that goal was achieved. Gilder lays out in the book, he believes that the cli- On the other hand, very little thought was ent–server model of current internet usage will be given to other dimensions of network security, succeeded by a peer-to-peer (P2P) model emp- and so, we have been playing Internet security loying blockchain: the technology that enabled catch-up ever since. (One might say that it is an ill the cryptocurrency bitcoin a decade ago. Another wind that blows no good, since Internet security YABB is IBM’s short Blockchain for Dummies jobs are in plentiful supply.) But as increasingly (https://public.dhe.ibm.com/common/ssi/ecm/xi/ more functions of society are transferred to the en/xim12354usen/ibm-blockchain_second- Internet, its lack of security has become a major edition_final_XIM12354USEN.pdf), which is avail- societal problem. Gilder and others believe that able for free download and has the more modest this lack of security is the Achilles heel of today’s goal of showing how blockchain for business ledg- Internet, and the reason that P2P architecture and ers is available now for practical use. In this paper, secure blockchain technology will supersede it. I will review some related characteristics of the The original Internet architecture was in fact Internet, of P2P, and of blockchain. Then, I will P2P. This simply means that any internet node describe how blockchain is “ready for business could both provide services to other nodes and/or use,” and finally, I will comment on its potential ask them to provide services. (For more depth, see impact on 21st century employment and business. https://en.m.wikipedia.org/wiki/Peer-to-peer.) For example, the file transfer protocol was/is bidirec- tional: Any node can send and/or receive files. As THE INTERNET, P2P, AND the Internet matured in the 1990s and 2000s, impor- BLOCKCHAIN tant services arose that were unidirectional, for One of the goals of the Internet architecture was example, Google searches, Amazon purchases, and to minimize points of failure. The switching centers Facebook friends. These services were provided that characterized the telephone network were by nodes that came to be called servers, and nodes such points of failure. If a switching center were to that utilized those services were called clients. be destroyed, the telephones in that area would The client–server model is subject to various security breaches (of course, so is P2P). For exam- Digital Object Identifier 10.1109/MITP.2018.2879244 ple, distributed denial-of-service attacks flood a Date of current version 26 February 2019. server with so many requests for service that it

January/February 2019 Published by the IEEE Computer Society 1520-9202 ß 2018 IEEE 2469-7087/20 © 2020 IEEE Published by the IEEE Computer Society January 2020 91 15 21mitp01-strawn-2879244.3d (Style 5) 14-10-2019 13:0

IT and Twenty-First Century Employment

shuts down. Also, most servers require usernames management, government, supply chain manage- and passwords from clients (and perhaps credit ment, and health care. Since these use cases card numbers). So, many users (me included) have replace (or at least reduce) the need for trusted hundreds of usernames and passwords, which I third party oversight, that reduction in employ- am supposed to remember and never write down. ment is obvious. Less obvious is the fact that many (This situation seems to me to provide more liabil- of these use cases can contain “smart contracts” ity protection for the server than security for the that automate various follow-on functions once a client.) And security breaches of servers are legion, transaction has been completed (e.g., automatic yielding crooks millions of usernames, passwords, payment once a shipment has been received). and credit card numbers. Such hacks are of incre- Thus, the need for fewer manual steps may extend well beyond transaction management. asing importance as online banking and other Perhaps even more important is that these use significant transactions are conducted online. cases typically take a significant amount of time to Blockchain technology is simply a distribu- complete. The use of blockchain could cut days ted ledger on a P2P network whose transactions and weeks to hours and minutes, and since time is cannot be erased or altered (see https://en.m. money, use of this technology could be doubly wikipedia.org/wiki/Blockchain for implementation cost-saving. Of course, in addition to increasing effi- details). As new transactions occur and are verified, ciency, effectiveness could be improved as well. As they are copied onto all copies of the ledger. It has Chapter Three of Dummies explains, blockchain been said that blockchain/P2P might to for tran- can reduce business network (information, interac- sactions what the Internet/Web did for Information. tion, and innovation) frictions in a number of ways. Share ledgers, P2P transactions, and smart con- BLOCKCHAIN FOR BUSINESS tracts are at the center of this business innovation. TRANSACTIONS In a recent blockchain report (https://public.dhe. Speaking of transactions, they are the busi- ibm.com/common/ssi/ecm/gb/en/gbe03835usen/ ness activity that blockchain is ready to facilitate, gbe03835usen-00_GBE03835USEN.pdf) drawing on according to IBM and others. Companies record 2965 conversations with C-suite executives, IBM transactions in ledgers, and traditionally, each reported the following industry statistics: Over company keeps its own ledger. Blockchain tech- one third of organizations across all industries nology enables a single, shared ledger for all the and regions are already considering or are companies engaging in related transactions. actively engaged with blockchain, and 66% of Moreover, this shared ledger has several pleasing early adopters—or explorers—intend to adopt a security characteristics. First, it is copied onto new platform business model that breaks the the computers of all participating companies, boundaries of traditional market exchanges. It making loss of data extremely unlikely. Second, would seem this train is moving. once a transaction has been “agreed to,” it cannot be changed or deleted. This provides a new level WHAT IS NEXT? of “technological trust” that has traditionally Blockchain burst on the scene as the bitcoin technology only ten years ago. It was the result been provided by trusted third parties. Regard- of innovative software, and hardware innova- ing how a transaction is agreed to, the business tions followed as the computationally expensive use just described requires only a simple vote by mining confirmation of transactions was opti- the companies involved rather than an expensive mized. On the other hand, 3-D printing (a.k.a. “mining” activity as in the bitcoin application. additive engineering) resulted from hardware innovations over 30 years ago and is now also USE CASES, EMPLOYMENT, AND moving (Amazon lists 100 books and gadgets on OTHER IMPLICATIONS the subject). As Moore’s and related laws con- Chapter four of Blockchain for Dummies tinue to lower the cost and raise the perfor- describes a plethora of transaction/ledger use mance of IT systems, predicting what is next cases the fall within the sphere of blockchain. requires matching new price points, innovative These use cases occur in a wide range of thinking, and society’s needs and desires. It is an areas: financial services, multinational policy exciting ride with far to go!

IT Professional 16 92 ComputingEdge January 2020

12.19 Computer Top Technology

DECEMBER 2019 DECEMBER Trends for 2020

Featured in Computer IEEE Computer Society tech experts unveil Technology their annual predictions for the future of Predictions tech. Six of the top 12 technology predictions have been developed into peer-reviewed articles published in Computer magazine’s December 2019 issue, covering topics TECHNOLOGY PREDICTIONS such as cognitive robotics, practical drone delivery, and digital twins.

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Smart Contracts

A Service-Oriented Perspective on Blockchain Smart Contracts

Florian Daniel Luca Guida Politecnico di Milano Politecnico di Milano

Abstract—Smart contracts turn blockchains into distributed computing platforms. This paper studies whether smart contracts as implemented by a state-of-the-art blockchain technology may serve as a component technology for a computing paradigm like service-oriented computing in the blockchain, in order to foster reuse and increase cost-effectiveness.

& A BLOCKCHAIN IS a shared, distributed ledger, Next to logging transactions, blockchain plat- that is, a log of transactions that provides for forms support the execution of pieces of code, persistency and verifiability of transactions.1 A so-called smart contracts,4,5 able to perform com- transaction is a cryptographically signed instruc- putations inside the blockchain. For example, a tion constructed by a user of the blockchain,2 smart contract may be used to automatically for example, the transfer of cryptocurrency fr- release a given amount of cryptocurrency upon om one account to another. Transactions are the satisfaction of a condition agreed on by two grouped into blocks, linked and secured using partners. If we put multiple smart contracts (and cryptographic hashes. A consensus protocol ena- partners) into communication, we turn the bles the nodes of the blockchain network to cre- blockchain into a proper distributed computing ate trust in the state of the log and makes platform.6 This makes the technology appealing blockchains inherently resistant to tampering.3 to application scenarios that ask for code execu- Thanks to these properties, blockchain technol- tion that is reliable, verifiable, and transactional. ogy is able to eliminate the need for a middleman For example, Xu et al.7 propose the use of from the management of transactions, such as a smart contracts as software connectors for reli- bank in the transfer of money. able, decentralized data sharing, while Weber et al.8 propose the integration of multiple smart contracts for distributed business process execution. The first example aims to support data Digital Object Identifier 10.1109/MIC.2018.2890624 providers in publishing data sets and data con- Date of current version 6 March 2019. sumers in finding and selecting data sets; using

1089-7801 ß 2019 IEEE Published by the IEEE Computer Society IEEE Internet Computing

Smart Contracts

cryptocurrency, data providers are automatic- helps to lower complexity but also increases cor- ally paid according to the value of the provided rectness by design. data, establishing an open, blockchain-based mar- ketplace for data. The second example generates smart contracts starting from a BPMN choreogra- BLOCKCHAIN AND SMART phy diagram and puts them into direct communi- CONTRACTS cation; the idea is to enable the execution of Next to Bitcoin, several alternative platforms business processes even among potentially have emerged over the last few years. Besides A Service-Oriented untrusted partners. The common ingredients of the type of cryptocurrency adopted as incentive both examples are smart contracts and verifiable mechanism, these platforms distinguish them- transactions. selves by few key properties. Perspective on Blockchain Developing applications that integrate multi- The access policy tells who can participate in ple smart contracts is however not easy, and the blockchain network. Public blockchains today’s predominant ad-hoc development prac- allow anyone to join and to access the informa- Smart Contracts tice will not be able to scale and be sustainable in tion stored in the blockchain via the Internet; pri- the long term. In fact, Atzei et al.9 show that vate blockchains are restricted to private already today even simple smart contracts are networks and selected nodes only. often affected by a variety of security vulnerabil- The validation policy tells who among the Florian Daniel Luca Guida ities. Nikolic et al.10 show that several of the smart nodes can participate in consensus creation and Politecnico di Milano Politecnico di Milano contracts deployed on Ethereum either “lock deploy smart contracts. Permissionless block- funds indefinitely, leak them carelessly to arbi- chains allow every node to perform both; permis- trary users, or can be killed by anyone.” Singh and sioned blockchains limit these capabilities to Abstract—Smart contracts turn blockchains into distributed computing platforms. Chopra11 go beyond implementation aspects and special nodes only, e.g., qualified through direct This paper studies whether smart contracts as implemented by a state-of-the-art discuss existing sociotechnical limitations of invitation. blockchain technology may serve as a component technology for a computing paradigm smart contracts, such as lack of control, lack of The consensus protocol specifies how trust is like service-oriented computing in the blockchain, in order to foster reuse and increase understanding, and lack of social meaning. created among participants: Proof of work (e.g., cost-effectiveness. We argue that future blockchain applications adopted by Bitcoin) requires nodes, so-called ask for abstractions, methods, and instruments miners, to invest significant hashing power to cre- that help developers to cope with complexity, ate trust. Proof of stake (Cardano) requires nodes & A BLOCKCHAIN IS a shared, distributed ledger, Next to logging transactions, blockchain plat- such as those proposed by service-oriented to prove ownership of sufficient cryptocurrency that is, a log of transactions that provides for forms support the execution of pieces of code, computing (SOC). In fact, the characteristics of to establish trust. Byzantine Fault Tolerance uses persistency and verifiability of transactions.1 A so-called smart contracts,4,5 able to perform com- the described data sharing scenario directly replication to establish trust in the state of the transaction is a cryptographically signed instruc- putations inside the blockchain. For example, a map to those of SOC (service provider, service network, even if faced with failing network nodes. tion constructed by a user of the blockchain,2 smart contract may be used to automatically consumer, service broker), yet smart contracts Variants are redundant BFT (Hyperledger Indy) for example, the transfer of cryptocurrency fr- release a given amount of cryptocurrency upon still lack equivalent support for description, dis- and practical BFT (Quorum), which aim at om one account to another. Transactions are the satisfaction of a condition agreed on by two covery, and the specification of nonfunctional increased redundancy and speed, respectively. grouped into blocks, linked and secured using partners. If we put multiple smart contracts (and properties. Similarly, the business process sce- Other notable consensus protocols are proof of cryptographic hashes. A consensus protocol ena- partners) into communication, we turn the nario resembles very much that of service-based elapsed time (Hyperledger Sawtooth), proof of bles the nodes of the blockchain network to cre- blockchain into a proper distributed computing business processes, yet the smart contracts gen- importance (NEM), proof of state (Universa Block- ate trust in the state of the log and makes platform.6 This makes the technology appealing erated in the scenario are tailored to specific chain Protocol), Raft-based consensus (Quorum), blockchains inherently resistant to tampering.3 to application scenarios that ask for code execu- tasks and partner interactions and are not stream-processing ordering services (Hyperledger Thanks to these properties, blockchain technol- tion that is reliable, verifiable, and transactional. directly applicable in processes with different Fabric), and Tempo (Radix DLT). ogy is able to eliminate the need for a middleman For example, Xu et al.7 propose the use of partners and/or choreography needs. That is, The choice of the consensus protocol affects from the management of transactions, such as a smart contracts as software connectors for reli- while they present significant opportunities for the transaction processing time (time till a trans- bank in the transfer of money. able, decentralized data sharing, while Weber reuse, they do not yet explore them. action is added to a block) and the transaction et al.8 propose the integration of multiple smart In the following, we thus look at smart con- rate (number of transactions processed per sec- contracts for distributed business process execu- tracts from an SOC perspective and study their ond). These properties and the access and tion. The first example aims to support data suitability as elementary pieces for a blockchain- validation policies determine a blockchain’s abil- Digital Object Identifier 10.1109/MIC.2018.2890624 providers in publishing data sets and data con- based, distributed computing paradigm. The ity to support different distributed computing Date of current version 6 March 2019. sumers in finding and selecting data sets; using assumption is that principled reuse not only scenarios.

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Smart Contracts

Table 1. Core characteristics of four example blockchain platforms.

Hyperledger Bitcoin Ethereum Corda Fabric No built-in No built-in Cryptocurrency Bitcoin (BTC) Ethereum (ETH) currency currency

Access policy Public Public Private Private

Validation policy Permissionless Permissionless Permissioned Permissioned

Proof of work Voting-based Validity consen- Consensus Proof of work (proof of stake algorithm sus, Uniqueness protocol under review�) (Apache Kafka) consensus

Transaction Almost Almost processing time 10 minutes 15 seconds � � instantaneous instantaneous (average)

Max transaction 7 TPS 20 TPS 3,500 TPS 170 TPS rate � � þ � Bitcoin Script, high-level Solidity, Serpent, languages JVM program- Smart contract lowlevel Lisp-like (BALZaC, BitML) Go ming languages language language (LLL), compilable to like Kotlin, Java Mutan Bitcoin native transactions

Turing No Yes Yes Yes completeness

[Online]. Available: https://cryptoslate.com/ethereums-proof-of-stake-protocol-in-review/

As for the implementation of smart contracts, knowledge and the goal of communicating some each platform typically supports one or more of the diversity that characterizes current block- programming languages. Some support general- chain technology. purpose languages, such as C, C , C#, F#, Go, þþ Java, JavaScript, Kotlin, Objective-C, PHP, SERVICE ORIENTATION Python, Rust, and Visual Basic .Net. Others pro- Service orientation is commonly associated pose platform-specific languages, such as Bit- with the binomial SOAP/WSDL or the REST archi- coin Script or Ethereum Solidity. The former are tectural style. Smart contracts use neither of Turing complete, the latter not necessarily (e.g., these, so we fall back to the generic definition by Bitcoin Script is not). Alonso et al.12 who define services as “com- In Table 1, we summarize these characteris- ponents that can be integrated into more com- tics for four platforms: Bitcoin (bitcoin.org), the plex distributed applications.” In order to first blockchain platform; Ethereum (ethereum. compare different web service technologies, org), the platform that first introduced Turing- Lagares Lemos et al.13 distinguish services by complete smart contracts; Hyperledger Fabric their type, interaction style, interaction proto- (hyperledger.org/projects/fabric), a private, per- col, data format, and descriptor. We discuss missioned platform hosted by the Linux Founda- these characteristics next for smart contracts, in tion and supported by more than 200 industry order to enable identifying analogies and differ- leaders; and Corda (corda.net), a private, per- ences between the proposed service-oriented missioned platform by a consortium of more interpretation of smart contracts and traditio- than 200 financial institutions and technology nal web service technologies. We specifically firms with a focus on interoperability. These focus on Ethereum as such is currently the most platforms represent an opportunistic selection used blockchain platform for smart contract (far from exhaustive) based on our own development.

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Smart Contracts

Contract Type Table 1. Core characteristics of four example blockchain platforms. obtain a list of accounts to send cryptocur- Components encapsulate data to be fetched rency to. Hyperledger Bitcoin Ethereum Corda and visualized or integrated and/or application Business-protocol-based interactions support Fabric logic to be interacted with. What the component patterns that may involve multiple interac- No built-in No built-in Cryptocurrency Bitcoin (BTC) Ethereum (ETH) delivers is a function of the type of the compo- currency currency tions and multiple clients or contracts; the nent. For smart contracts we can distinguish the protocol speciﬁes the order of interactions Access policy Public Public Private Private following contract types: and the roles of the involved parties. Validation policy Permissionless Permissionless Permissioned Permissioned As running smart contracts costs money, Generic contracts implement application Proof of work Voting-based Validity consen- contracts are activated only in response to Consensus Proof of work (proof of stake algorithm sus, Uniqueness logic, e.g., for deposit management, that can protocol explicit invocations. A contract or a group of under review�) (Apache Kafka) consensus be invoked by blockchain clients or by other interacting contracts is thus always triggered by Transaction contracts; in general, this type of contract is Almost Almost a client transaction, and independent, active processing time 10 minutes 15 seconds stateful in that it maintains application state � � instantaneous instantaneous behaviors are typically not supported. (average) across interactions.

Max transaction Libraries implement one or more functions, 7 TPS 20 TPS 3,500 TPS 170 TPS Interaction Protocol rate � � þ � e.g., a math library, that are meant for reuse This tells how a component implements its Bitcoin Script, by other contracts; libraries do not store interactions. Conventional web services use high-level internal variables and are stateless. Solidity, Serpent, message-oriented protocols such as SOAP or languages JVM program- Smart contract lowlevel Lisp-like (BALZaC, BitML) Go ming languages Data contracts provide data storage services HTTP, while all major programming languages language language (LLL), compilable to like Kotlin, Java also support RPC-like interactions (Remote Pro- Mutan inside the blockchain, e.g., a client references Bitcoin native cedure Calls). Ethereum uses a message-based transactions manager, that are meant for use by other contracts; by design, they are stateful. protocol supporting the following interaction Turing No Yes Yes Yes features: completeness Oracles deliver data services from the out- Transactions are used by blockchain cli- side of the blockchain to the inside of the [Online]. Available: https://cryptoslate.com/ethereums-proof-of-stake-protocol-in-review/ ents (the users of the blockchain) to cre- blockchain, e.g., currency conversation ate new contracts or to invoke existing rates. Contracts cannot make calls outside As for the implementation of smart contracts, knowledge and the goal of communicating some contracts; once validated, which consumes the blockchain, as outside dependencies each platform typically supports one or more of the diversity that characterizes current block- cryptocurrency, transactions are added may prevent verifiability (conversion rates programming languages. Some support general- chain technology. to the blockchain and remain publicly change over time). If data from the outside is purpose languages, such as C, C , C#, F#, Go, accessible. þþ needed, it can be pushed by clients to Java, JavaScript, Kotlin, Objective-C, PHP, SERVICE ORIENTATION Events enable a contract to push infor- oracles using transactions; these then allow Python, Rust, and Visual Basic .Net. Others pro- Service orientation is commonly associated mation to the outside world in response other contracts to query for the data. pose platform-specific languages, such as Bit- with the binomial SOAP/WSDL or the REST archi- to a transaction invoking the contract; coin Script or Ethereum Solidity. The former are tectural style. Smart contracts use neither of when the transaction is added to the Turing complete, the latter not necessarily (e.g., these, so we fall back to the generic definition by Interaction Style blockchain, also the event becomes pub- Bitcoin Script is not). Alonso et al.12 who define services as “com- Integrating a component into a composite licly accessible.

In Table 1, we summarize these characteris- ponents that can be integrated into more com- application usually does not only involve a one- Calls (so-called message calls) are used by tics for four platforms: Bitcoin (bitcoin.org), the plex distributed applications.” In order to shot query or call. It may be necessary to inter- contracts to interact with each other in a ﬁrst blockchain platform; Ethereum (ethereum. compare different web service technologies, act with the component multiple times and to fashion that uses different state spaces for org), the platform that ﬁrst introduced Turing- Lagares Lemos et al.13 distinguish services by establish some form of conversation with it. For each contract for isolation; calls are exe- complete smart contracts; Hyperledger Fabric their type, interaction style, interaction proto- smart contracts we have: cuted locally to each blockchain node and

(hyperledger.org/projects/fabric), a private, per- col, data format, and descriptor. We discuss Pull interactions enable a client or contract do not consume cryptocurrency. missioned platform hosted by the Linux Founda- these characteristics next for smart contracts, in to initiate an interaction and to invoke a con- Delegate calls are used by contracts to invoke tion and supported by more than 200 industry order to enable identifying analogies and differ- tract that otherwise would be passive; for libraries in a fashion where functions are exe- leaders; and Corda (corda.net), a private, per- ences between the proposed service-oriented instance, a client may invoke a contract to cuted in one, the caller’s, state space; dele- missioned platform by a consortium of more interpretation of smart contracts and traditio- withdraw a deposit. gate calls too are node-local and do not than 200 financial institutions and technology nal web service technologies. We specifically Push interactions enable the contract to consume cryptocurrency. firms with a focus on interoperability. These focus on Ethereum as such is currently the most become active and to initiate an interaction If an interaction originates from a blockchain platforms represent an opportunistic selection used blockchain platform for smart contract with clients or other contracts; for instance, client, it uses JSON-RPC or is enacted using the (far from exhaustive) based on our own development. a contract may invoke a data contract to command line; if it originates from a smart

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Smart Contracts

contract, the message is exchanged via RPC. languages such as OWL-S, WSDL-S, and WSMO Transactions contain a set of predefined param- are used to describe service endpoints, opera- eters: the number of transactions sent by the tions, and data formats. sender, the amount of cryptocurrency the The construct that gets closest to a sender is willing to pay for consumed resources description of Ethereum smart contracts is (so-called gas), the maximum consumable the so-called “ABI in JSON” interface descrip- amount of gas, the address of the recipient, the tion produced by the Solidity compiler during amount of cryptocurrency to be transferred, compilation, as exemplified by the following possible signatures of the sender, and either lines of code: the code of the contract to be created or input [{ data to be processed. Events contain, among “type”: “function”, others, one or more topics that allow clients to “inputs”: [{“name”: “username”, “type”: “string”}, search for and subscribe to events and a data {“name”: “password”, “type”: “string”}], field. Calls contain the sender and receiver “name”: “create_user”, “outputs”: [{“name”: “success”, “type”: “bool”}] addresses, a possible value and data; calls may }, { return a value. “type”: “event”, “inputs”: [{“name”: “username”, “type”: “string”, Data Format “indexed”: true}, The data format determines how exchanged {“name”: “count”, “type”: “uint256”, data is formatted. Message-oriented interaction “indexed”: false}], “name”: “user_created” protocols typically support self-describing docu- }] ment formats like XML and JSON; RPC-oriented protocols enable the exchange of native data The description specifies one function structures, such as Java or JavaScript objects, (create_user) and one event (user_created), using an internal, binary format hidden to along with their inputs and outputs. The inputs developers. of the event are their publicly accessible argu- Data in Ethereum transactions and events is ments stored in the blockchain; indexed argu- encoded using the Application Binary Interface ments are searchable. What this description (ABI), which specifies how functions are called does not include is the name of the contract, its and data are formatted. Clients either serialize address, the network/chain ID if the contract is data in a binary format on their own, e.g., when deployed on a test network, and non-functional using the command line or by using a suitable properties (e.g., the cost of invoking the func- library function, e.g., the function toPayload of tion). These are essential for search and discov- the library web3.js. Values are encoded in ery. Also, Ethereum does not come with a sequential order and according to their data registry for smart contracts, although contract types and are not self-describing. In order to metadata (containing the ABI in JSON descrip- allow the receiver to identify which function is tion) can be stored in Swarm, a redundant called, the sequence of values is preceded by 4 B and decentralized store of Ethereum’s public of a Keccak-256 hash of the respective function record. signature. This allows everybody to parse the binary formatted data. Data in message/delegate calls between con- STATE OF TECHNOLOGY tracts is exchanged by passing variables, mask- In Table 2, we summarize how these SOA ing the underlying ABI formatting. characteristics are manifest (or not) in the four platforms we introduced earlier. Description As expected, Bitcoin is the most limited plat- The final aspect of components is component form in terms of features supported when it description, which enables discovery and selec- comes to smart contracts. In fact, it was born as tion. For web services, description languages support for its homonymous cryptocurrency such as WSDL and WADL and semantics-oriented and less to support generic computations.

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Smart Contracts

contract, the message is exchanged via RPC. languages such as OWL-S, WSDL-S, and WSMO Table 2. SOC perspective on selected smart contract technologies. Transactions contain a set of predefined param- are used to describe service endpoints, opera- Hyperledger Bitcoin Ethereum Corda eters: the number of transactions sent by the tions, and data formats. Fabric sender, the amount of cryptocurrency the The construct that gets closest to a Contracts, Contracts sender is willing to pay for consumed resources description of Ethereum smart contracts is Contracts, libraries, data Contracts, Contract type (chaincode), data oracles contracts, libraries, oracles (so-called gas), the maximum consumable the so-called “ABI in JSON” interface descrip- contracts oracles amount of gas, the address of the recipient, the tion produced by the Solidity compiler during Pull and push Pull and push amount of cryptocurrency to be transferred, compilation, as exemplified by the following Pull interactions, Pull and push interactions, Interaction style possible signatures of the sender, and either lines of code: interactions business interactions business the code of the contract to be created or input protocols protocols [{ Transactions, data to be processed. Events contain, among “type”: “function”, Transactions, calls (limited to others, one or more topics that allow clients to “inputs”: [{“name”: “username”, “type”: “string”}, inter-node Transactions, contracts on messages search for and subscribe to events and a data {“name”: “password”, “type”: “string”}], Interaction events, message same node and Transactions (so-called flows), “name”: “create_user”, protocol calls, delegate channel), field. Calls contain the sender and receiver scheduled “outputs”: [{“name”: “success”, “type”: “bool”}] calls events—exposes invocations of addresses, a possible value and data; calls may REST APIs toward }, { contracts return a value. “type”: “event”, these “inputs”: [{“name”: “username”, “type”: “string”, Binary payloads Any type of the “indexed”: true}, in transactions contract Data Format Binary or JSON Binary payloads and events, language, zip The data format determines how exchanged {“name”: “count”, “type”: “uint256”, Data format formatted in transactions Solidity data attachments “indexed”: false}], key-value pairs data is formatted. Message-oriented interaction types in message/ referenced using “name”: “user_created” protocols typically support self-describing docu- delegate calls hashes }] ment formats like XML and JSON; RPC-oriented Contract Chaincode protocols enable the exchange of native data metadata (JSON) metadata with The description specifies one function No contract to be published interfaces, No contract Description structures, such as Java or JavaScript objects, (create_user) and one event (user_created), description on a public endpoints, and description using an internal, binary format hidden to along with their inputs and outputs. The inputs storage platform interaction (e.g., Swarm) schemas developers. of the event are their publicly accessible argu- Data in Ethereum transactions and events is ments stored in the blockchain; indexed argu- encoded using the Application Binary Interface ments are searchable. What this description (ABI), which specifies how functions are called does not include is the name of the contract, its Ethereum, on the other hand, is the most com- Ethereum message/delegate calls pass native and data are formatted. Clients either serialize address, the network/chain ID if the contract is plete platform, with Hyperledger Fabric and Solidity data structures. Hyperledger Fabric data in a binary format on their own, e.g., when deployed on a test network, and non-functional Corda providing comparable features. structures data as key-value pairs in binary and/ using the command line or by using a suitable properties (e.g., the cost of invoking the func- In terms of contract types, all platforms sup- or JSON format. Corda, in addition to generic library function, e.g., the function toPayload of tion). These are essential for search and discov- port oracles, except Hyperledger Fabric for Kotlin/Java data objects, also supports transac- the library web3.js. Values are encoded in ery. Also, Ethereum does not come with a which so-called “gateway services” are still tions with generic attachments; attachments are sequential order and according to their data registry for smart contracts, although contract under discussion (as of June 2018). Reusable zipped and hash referenced. types and are not self-describing. In order to metadata (containing the ABI in JSON descrip- code libraries are supported only by Ethereum As for the description of smart contracts for allow the receiver to identify which function is tion) can be stored in Swarm, a redundant and Corda. It is important to note that contracts search and reuse, support is very limited. Only called, the sequence of values is preceded by 4 B and decentralized store of Ethereum’s public generally encapsulate application logic; data Ethereum and Hyperledger Fabric provide basic of a Keccak-256 hash of the respective function record. contracts are typically very limited in their stor- metadata describing a contract’s interface (oper- signature. This allows everybody to parse the age capacity, as storing data on the blockchain ations and arguments), but we are far from a binary formatted data. may incur significant costs. common description format let alone a registry Data in message/delegate calls between con- STATE OF TECHNOLOGY All platforms except Bitcoin support pull and for the discovery of contracts. tracts is exchanged by passing variables, mask- In Table 2, we summarize how these SOA push interactions; Bitcoin features only client- ing the underlying ABI formatting. characteristics are manifest (or not) in the four originated pull transactions. Looking at the DISCUSSION AND OUTLOOK platforms we introduced earlier. interaction protocols, Ethereum, Hyperledger By now, there is a general consensus that the Description As expected, Bitcoin is the most limited plat- Fabric, and Corda support transactions, calls impact of blockchain will go far beyond crypto- The final aspect of components is component form in terms of features supported when it between contracts, and events; Bitcoin has only currencies, possibly with disruptive effects on description, which enables discovery and selec- comes to smart contracts. In fact, it was born as transactions. distributed application development.14 The key tion. For web services, description languages support for its homonymous cryptocurrency Payload data is binary formatted in Bitcoin enabler for this impact is smart contracts able to such as WSDL and WADL and semantics-oriented and less to support generic computations. and Ethereum transactions and events, while support a new kind of distributed computing.6

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Smart Contracts

While the number and types of platforms for shared interaction styles and protocols smart contracts are constantly growing—this as well as data formats and, of course, paper studies four of them, dozens of others authentication and certiﬁcation mechanisms. have emerged—the resulting technological land- A particular challenge is cross-blockchain scape is getting increasingly intricate and integration.

heterogeneous. Composition: Finally, in order to be able to Yet, this paper shows that from an exploit the full power of smart contracts application point of view the conceptual under- (and to collectively save resources and pinnings of this new landscape are more inte- money) it is necessary to conceive and imple- grated than one would expect and that smart ment composition solutions able to abstract contracts, to some extent, may indeed be inter- away from technicalities and to provide preted as elementary pieces, that is, services, developers with instruments and infrastruc- of a blockchain-based, SOC paradigm. The tures that enhance productivity effectively. paper, however, also shows that we are still far from a smart contract model that sees interop- In short, what we envision is an evolution from erability and reusability as beneﬁcial features, today’s technology silos to an abstract, reuse- as instead we are used to in the context of SOC. oriented contract ecosystem able to preserve the In order to enable service orientation in guarantees proper of blockchain technology. blockchain and to unleash the full power of smart contracts, several challenges need to be & REFERENCES faced, among which we mention: 1. S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash Search, discovery and reuse: It is striking that system,” 2008, [Online]. Available: https://bitcoin.org/ so little attention has been paid so far to en/bitcoin-paper enable developers to reuse already deployed 2. G. Wood, “Ethereum: A secure decentralised contracts, especially if we consider that generalised transaction ledger,” Ethereum Project deploying a new contract is typically more Yellow Paper, vol. 151, pp. 1–32, 2014. cost-intensive then just invoking an already 3. D. Mingxiao, M. Xiaofeng, Z. Zhe, W. Xiangwei, and deployed one. Suitable abstract descriptors C. Qijun, “A review on consensus algorithm of and searchable registries are badly needed. blockchain,” in Proc. IEEE Int. Conf. Syst., Man, Cost awareness: Smart contracts natively Cybern., 2017, pp. 2567–2572. incorporate the concept of resource con- 4. N. Szabo, “Smart contracts: Building blocks for digital sumption and cost of invocations. It is crucial markets,” EXTROPY: J. Transhumanist Thought, 1996, that smart contracts be able to properly Art. no. 16. communicate and negotiate these kinds of 5. R. M. Parizi and A. Dehghantanha, “Smart contract service levels, enabling a natural pay-per- programming languages on blockchains: An empirical invocation model. evaluation of usability and security,” in Proc. Int. Conf.

Performance: Libraries and data contracts Blockchain, 2018, pp. 75–91. are executed locally inside each node and 6. B. Dickson, “How blockchain can create the world’s have thus negligible response times; oracles biggest supercomputer,” TechCrunch, Dec. 2016. and generic contracts, which may require 7. X. Xu et al., “The blockchain as a software connector,” transaction processing, may lead to higher, in Proc. 13th Work. IEEE/IFIP Conf. Softw. Archit., unpredictable response times. The challenge 2016, pp. 182–191. is improving performance in terms of trans- 8. I. Weber, X. Xu, R. Riveret, G. Governatori, A. Ponomarev, action rates and processing times. and J. Mendling, “Untrusted business process

Interoperability and standardization: Today, monitoring and execution using blockchain,” In Proc. Int. platforms concentrate on own technologies Conf. Bus. Process Manage., 2016, pp. 329–347. as distinguishing feature, which is under- 9. N. Atzei, M. Bartoletti, and T. Cimoli, “A survey of standable. This, however, slows down inte- attacks on Ethereum smart contracts (sok),” in gration, which eventually will nevertheless Principles of Security and Trust. Berlin, Germany: be needed. The challenge is agreeing on Springer, 2017, pp. 164–186.

IEEE Internet Computing 24 52 ComputingEdge January 2020 23mic01-daniel-2890624.3d (Style 5) 14-10-2019 11:32 23mic01-daniel-2890624.3d (Style 5) 14-10-2019 11:32

Smart Contracts

is an Associate Professor with Poli- While the number and types of platforms for shared interaction styles and protocols 10. I. Nikolic, A. Kolluri, I. Sergey, P. Saxena, and Florian Daniel tecnico di Milano, Milan, Italy. His research interests smart contracts are constantly growing—this as well as data formats and, of course, A. Hobor, “Finding the greedy, prodigal, and suicidal include service-oriented computing, blockchain, paper studies four of them, dozens of others authentication and certification mechanisms. contracts at scale,” arXiv:1802.06038, 2018. business process management, and data science. have emerged—the resulting technological land- A particular challenge is cross-blockchain 11. M. P. Singh and A. K. Chopra, “Violable contracts and He received the Ph.D. degree in information technol- scape is getting increasingly intricate and integration. governance for blockchain applications,” ogy from Politecnico di Milano. Contact him at florian. arXiv:1801.02672, 2018. heterogeneous. Composition: Finally, in order to be able to [email protected]. Yet, this paper shows that from an exploit the full power of smart contracts 12. G. Alonso, F. Casati, H. Kuno, and V. Machiraju, Web application point of view the conceptual under- (and to collectively save resources and Services. Berlin, Germany: Springer, 2004. Luca Guida is a graduate of Politecnico di Milano, pinnings of this new landscape are more inte- money) it is necessary to conceive and imple- 13. A. Lagares Lemos, F. Daniel, and B. Benatallah, “Web Milan, Italy. His research interests include block- grated than one would expect and that smart ment composition solutions able to abstract service composition: A survey of techniques and tools,” chain, service-oriented computing, and spatial data contracts, to some extent, may indeed be inter- away from technicalities and to provide ACM Comput. Surveys, vol. 48, no. 3, Feb. 2016, Art. no. 33. analysis. He received the master’s degree (cum laude) in computer science and engineering from preted as elementary pieces, that is, services, developers with instruments and infrastruc- 14. L. Mearian, “What is blockchain? The most disruptive Politecnico di Milano and Alta Scuola Politecnica, of a blockchain-based, SOC paradigm. The tures that enhance productivity effectively. tech in decades.” IDG Commun., Inc., Framingham, Milan, Italy. Contact him at [email protected]. paper, however, also shows that we are still far MA, USA, May 2018. from a smart contract model that sees interop- In short, what we envision is an evolution from erability and reusability as beneficial features, today’s technology silos to an abstract, reuse- as instead we are used to in the context of SOC. oriented contract ecosystem able to preserve the In order to enable service orientation in guarantees proper of blockchain technology. This article originally appeared in blockchain and to unleash the full power of IEEE Internet Computing, vol. 23, no. 1, 2019. smart contracts, several challenges need to be & REFERENCES faced, among which we mention: 1. S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash Search, discovery and reuse: It is striking that system,” 2008, [Online]. Available: https://bitcoin.org/ so little attention has been paid so far to en/bitcoin-paper enable developers to reuse already deployed 2. G. Wood, “Ethereum: A secure decentralised contracts, especially if we consider that generalised transaction ledger,” Ethereum Project deploying a new contract is typically more Yellow Paper, vol. 151, pp. 1–32, 2014. ADVERTISER INFORMATION cost-intensive then just invoking an already 3. D. Mingxiao, M. Xiaofeng, Z. Zhe, W. Xiangwei, and deployed one. Suitable abstract descriptors C. Qijun, “A review on consensus algorithm of and searchable registries are badly needed. blockchain,” in Proc. IEEE Int. Conf. Syst., Man, Cost awareness: Smart contracts natively Cybern., 2017, pp. 2567–2572. Advertising Coordinator Central US, Northwest US, Southeast US, Asia/Pacific: incorporate the concept of resource con- 4. N. Szabo, “Smart contracts: Building blocks for digital Eric Kincaid sumption and cost of invocations. It is crucial markets,” EXTROPY: J. Transhumanist Thought, 1996, Debbie Sims Email: [email protected] that smart contracts be able to properly Art. no. 16. Email: [email protected] Phone: +1 214-553-8513 | Fax: +1 888-886-8599 Phone: +1 714-816-2138 | Fax: +1 714-821-4010 Cell: +1 214-673-3742 communicate and negotiate these kinds of 5. R. M. Parizi and A. Dehghantanha, “Smart contract service levels, enabling a natural pay-per- programming languages on blockchains: An empirical Midwest US: invocation model. evaluation of usability and security,” in Proc. Int. Conf. Advertising Sales Contacts Dave Jones Email: [email protected] Performance: Libraries and data contracts Blockchain, 2018, pp. 75–91. Mid-Atlantic US: Phone: +1 708-442-5633 Fax: +1 888-886-8599 are executed locally inside each node and 6. B. Dickson, “How blockchain can create the world’s Dawn Scoda Cell: +1 708-624-9901 have thus negligible response times; oracles biggest supercomputer,” TechCrunch, Dec. 2016. Email: [email protected] and generic contracts, which may require 7. X. Xu et al., “The blockchain as a software connector,” Phone: +1 732-772-0160 Cell: +1 732-685-6068 | Fax: +1 732-772-0164 Jobs Board (West Coast and Asia), Classified Line Ads transaction processing, may lead to higher, in Proc. 13th Work. IEEE/IFIP Conf. Softw. Archit., unpredictable response times. The challenge 2016, pp. 182–191. Southwest US, California: Heather Bounadies is improving performance in terms of trans- 8. I. Weber, X. Xu, R. Riveret, G. Governatori, A. Ponomarev, Mike Hughes Email: [email protected] action rates and processing times. and J. Mendling, “Untrusted business process Email: [email protected] Phone: +1 623-233-6575 Cell: +1 805-208-5882 Interoperability and standardization: Today, monitoring and execution using blockchain,” In Proc. Int. platforms concentrate on own technologies Conf. Bus. Process Manage., 2016, pp. 329–347. Northeast, Europe, the Middle East and Africa: Jobs Board (East Coast and Europe), SE Radio Podcast as distinguishing feature, which is under- 9. N. Atzei, M. Bartoletti, and T. Cimoli, “A survey of David Schissler Email: [email protected] standable. This, however, slows down inte- attacks on Ethereum smart contracts (sok),” in Marie Thompson Phone: +1 508-394-4026 Email: [email protected] gration, which eventually will nevertheless Principles of Security and Trust. Berlin, Germany: Phone: +1 714-813-5094 be needed. The challenge is agreeing on Springer, 2017, pp. 164–186.

IEEE Internet Computing January/February 2019 52 www.computer.org/computingedge 53 25 COMPSAC 2020 Madrid, Spain July 13-17, 2020

COMPSAC is the IEEE Computer Society Signature Conference on Computers, Software and Applications. It is a major international forum for academia, industry, and government to discuss research results and advancements, emerging challenges, and future trends in computer and software technologies and applications. The theme of COMPSAC 2020 is “Driving Intelligent Transformation of the Digital World”. Staying relevant in a constantly evolving digital landscape is a challenge faced by researchers, developers, and producers in virtually every industry and area of study. Once limited to software-enabled devices, the ubiquity of digitally-enabled systems makes this challenge a universal issue. Furthermore, as relevance fuels change, many infl uencers will off er solutions that benefi t their own priorities. Fortunately, history has shown that the building blocks of digital change are forged by those conducting foundational research and development of digital systems and human interactions. Artifi cial Intelligence is not new, but is much more utilized in everyday computing now that data and processing resources are more economically viable, hence widely available. The opportunity to drive the use of this powerful tool in transforming the digital world is yours. Will your results help defi ne the path ahead, or will you relegate those decisions to those with diff erent priorities for utilizing intelligence in digital systems? COMPSAC has been and continues to be a highly respected venue for the dissemination of key research on computer and software systems and applications, and has infl uenced fundamental developments in these fi elds for over 40 years. COMPSAC 2020 is your opportunity to add your mark to this ongoing journey, and we highly encourage your submission! COMPSAC 2020, organized as a tightly integrated union of symposia, will focus on technical aspects of issues relevant to intelligent transformation of the digital world. The technical program will include keynote addresses, research papers, industrial case studies, fast abstracts, a doctoral symposium, poster sessions, and workshops and tutorials on emerging and important topics related to the conference theme. Highlights of the conference will include plenary and specialized panels that will address the technical challenges facing researchers and practitioners who are driving fundamental changes in intelligent systems and applications. Panels will also address cultural and societal challenges for a society whose members must continue to learn to live, work, and play in the environments the technologies produce. Authors are invited to submit original, unpublished research work, as well as industrial practice reports. Simultaneous submission to other publication venues is not permitted except as highlighted in the COMPSAC 2020 J1C2 & C1J2 program. All submissions must adhere to IEEE Publishing Policies, and will be vetted through the IEEE CrossCheck portal. Further info is available at www.compsac.org. Organizers Standing Committee Chair: Sorel Reisman (California State University, USA) Steering Committee Chair: Sheikh Iqbal Ahamed (Marquette University, USA) General Chairs: Mohammad Zulkernine (Queen’s University, Canada), Edmundo Tovar (Universidad Politécnica de Madrid, Spain), Hironori Kasahara (Waseda University, Japan) Program Chairs in Chief: W. K. Chan (City University, Hong Kong), Bill Claycomb (Carnegie Mellon University, USA), Hiroki Takakura (National Institute of Informatics, Japan) Workshop Chairs: Ji-Jiang Yang (Tsinghua University, USA), Yuuichi Teranishi (National Institute of Information and Communications Technology, Japan), Dave Towey (University of Nottingham Ningbo China, China), Sergio Segura (University of Seville, Spain) Local Chairs: Sergio Martin (UNED, Spain), Manuel Castro (UNED, Spain)

Important Dates Workshops proposals due: 15 November 2019 Workshops acceptance notifi cation: 15 December 2019 Main conference papers due: 20 January 2020 Paper notifi cation: 3 April 2020 Workshop papers due: 9 April 2020 Workshop paper notifi cations: 1 May 2020 Photo: King Felipe III in Major Square, Madrid Camera-ready and registration due: 15 May 2020 Photo credit: Iria Castro - Photographer (Instagram @iriacastrophoto) EDITOR JEFFREY VOAS CYBERTRUST NIST; [email protected]

Cryptocurrencies: Transparency Versus Privacy

Nir Kshetri, University of North Carolina at Greensboro that provide reasonable levels of privacy to users. To make the costs of Cryptocurrencies can have signiﬁ cant privacy transparency less severe to privacy, costs. A motivated adversary has available Bitcoin and other cryptocurrencies employ pseudonymity. Users can a range of actions to identify the actual user conduct transactions with one another without disclosing any infor- associated with a cryptocurrency account. By mation related to their identity. Concealing the Internet Protocol (IP) taking appropriate measures, cryptocurrency addresses of users is another mech- users can minimize privacy violations and anism that provides protection to cryptocurrency user privacy. For ex- reduce the risk of privacy breaches. ample, in the Bitcoin network, cor- respondence cannot be established be tween transactions and IP addresses. ransparency is a major factor that is driving Bitcoin users are connected to a peer-to-peer (P2P) network. the use of blockchain-based applications such Data continue to fl ow among the devices connected to the as cryptocurrencies. A major question becomes P2P network until everyone has the information related to whether transparency provides reasonable pri- a transaction. No one, except for the originator, knows who Tvacy protection. For instance, many fi rms in the fi nancial initiated the transaction.1 sector do not like the fact that blockchain’s transparent nature gives other users access to the details of conducted CONSEQUENCES OF PRIVACY VIOLATIONS transactions. IN THE CRYPTO-WORLD Let’s begin with cryptocurrencies. It is important to Individuals and organizations are likely to suff er more se- note that cryptocurrencies possess built-in mechanisms vere consequences from cases of privacy violation if they en- gage in illegal behaviors using cryptocurrencies (compared with other transaction models). For example, if someone is Digital Object Identifier 10.1109/MC.2018.2876182 Date of publication: 15 January 2019 caught in a crime, the cryptocurrency account can be linked

to any crime committed by that person Blockchain ledgers are searchable CRYPTOCURRENCIES in the past. Privacy breaches are likely and, hence, can be used to track transac- HAVE DIFFERENT to lead to more severe criminal conse- tions.5 If a leak involves the amount and LEVELS OF PRIVACY quences, referred as an amplified techni- time of the purchase, a motivated adver- Well-known cryptocurrencies such as cal impact.2 sary can convert the purchase amount Bitcoin have not been able to meet all privacy needs of users. As mentioned, financial firms are concerned that blockchain’s ledger allows other users to access the details of transactions al- Zcash transactions have two types of addresses: ready conducted. In response to these transparent and shielded. demands, some cryptocurrencies provide users with higher levels of pri - vacy protection. Privacy is important for citizens and into Bitcoins using the exchange rate Blockchain is still in early-stage businesses. If an individual uses Bitcoin at that time. Then, a blockchain can development, and various alternative to pay for certain goods or services, the be searched for a transaction of that models and forms of cryptocurren- party with whom the transaction is be- amount and at that time. This gives away cies are evolving along with it. For ing made can know exactly how much the user’s Bitcoin address. Any other instance, to make blockchain more ap- money the individual has. This may purchases made using that address are pealing to financial institutions, the cryp- increase the threat to personal safety. now easier to trace.4 tocurrency Zcash, which was launched A supplier that has received a payment Sometimes, an act of carelessness in October 2016, has promised transac- from a business would know how much on the part of the user may decrease tional privacy.6 It employs cryptogra- money the business has. Knowing fund privacy. This happened to Ross Ul - phy to enhance user privacy. availability and customer price sensi- bricht, who created the online black Zcash transactions can be made tivity could affect future negotiations. market Silk Road, best known as a plat- transparent, like those of Bitcoin, or Finally, if online businesses have infor- form for selling illegal drugs. When shielded through a zero-knowledge mation about a consumer’s spending Ulbricht looked for help to expand the proof. Zcash transactions have two patterns, they could predict the high- Silk Road business, he used the same types of addresses: transparent and est price that the consumer could pay. pseudonym that he had adopted pre- shielded. In transparent addresses, as The business could then use price tam- viously to post announcements on ille- is the case for Bitcoin, the monetary pering to increase profits.3 gal drug discussion forums. This made amount of the transaction as well as him an FBI suspect. The FBI tracked information about the receiver and the UNWANTED PERSONAL his IP address to an Internet café in sender appears in the blockchain. On INFORMATION LEAKS San Francisco and caught Ulbricht as the other hand, if a shielded address is There are various sources of unwanted he was logging in to Silk Road as an used, the address is “obscured” on the personal information leaks in transac- administrator.1 public ledger. Also, if both the sender tions involving cryptocurrencies. For Another privacy problem oc - and the receiver use shielded addresses, instance, while Bitcoin transactions curs when users of cryptocurrencies the transaction amount is encrypted. are difficult to track, they are not- com such as Bitcoin reuse addresses. By Users of shielded addresses con- pletely anonymous. All transactions are doing so, they publicly disclose infor- stitute a small proportion of Zcash recorded in a permanent public ledger. mation about past financial transac- adopters. In early 2017, shielded ad - After the Bitcoins are moved from that tions, and this can compromise their dresses accounted for about 0.8% of address, financial movements can be privacy. The transparency and im- Zcash transactions.7 That propor- traced. Users can be traced through IP mutability features of cryptocur - tion is predicted to increase to 4% by addresses and money flows. A team of rencies like Bitcoin make it possible mid-2018.8 researchers studied 130 major merchants to track every transaction involving A relatively low adoption rate of that allow Bitcoin transactions. They a given address. Even if a person has shielded addresses might be due to the found that at least 53 of the merchants engaged in careful processes to hide additional time and computational re- leaked payment information to at least 40 his or her identity, once a link has sources required. Shielded addresses third parties. While most of the informa- been established between a person’s require a more computationally in- tion leaked was intentional and used for identity and a Bitcoin address, all past tensive process. To use Zcash’s pri- advertising and analytics, some merchant transactions made by the owner of the vacy features, users may need 4 GB or websites also leaked precise blockchain Bitcoin address will be associated to more of RAM (tinyurl.com/y9dtj3dh). transaction information to trackers.4 the owner’s identity. With 4 GB of RAM, operations were

28 ComputingEdge January 2020 100 COMPUTER WWW.COMPUTER.ORG/COMPUTER CYBERTRUST

to any crime committed by that person Blockchain ledgers are searchable CRYPTOCURRENCIES reported to take as long as 2 min to can trace transactions to individuals REFERENCES in the past. Privacy breaches are likely and, hence, can be used to track transac- HAVE DIFFERENT complete.9 Therefore, most exchanges and groups. Elliptic’s services are used 1. J. Bohannon. (2016, Mar. 9). Why to lead to more severe criminal conse- tions.5 If a leak involves the amount and LEVELS OF PRIVACY and wallets support only transparent by online exchanges and law enforce- criminals can’t hide behind quences, referred as an amplified techni- time of the purchase, a motivated adver- Well-known cryptocurrencies such as Zcash transactions.8 ment to detect money laundering (bit Bitcoin. Science. [Online]. Avail- cal impact.2 sary can convert the purchase amount Bitcoin have not been able to meet all Likewise, Monero focuses on privacy .ly/1T3SBwc). able: http://www.sciencemag.org/ privacy needs of users. As mentioned, and untraceability by hiding the transac- The higher levels of privacy offered news/2016/03/why-criminals-cant- financial firms are concerned that tion’s sender, receiver, and monetary by cryptocurrencies such as Monero hide-behind-bitcoin blockchain’s ledger allows other users amount. To achieve this, Monero mixes and Zcash concern regulators who 2. ISACA, Generating value from to access the details of transactions al- Monero “coins” with other forms of pay- are focused on money laundering. A big data analytics, ISACA, Zcash transactions have two types of addresses: ready conducted. In response to these ments. This makes it nearly impossible cybercrime expert at the European Schaumburg, IL, White Paper, transparent and shielded. demands, some cryptocurrencies pro- to link a transaction to any particular Union’s law enforcement agency, Eu- 2014. Available: http://www.isaca vide users with higher levels of pri - identity or previous transaction from the ropol, noted that criminals have be- .org/Knowledge-Center vacy protection. same source if only Monero’s blockchain gun shifting away from Bitcoin to /Research/ResearchDeliverables Privacy is important for citizens and into Bitcoins using the exchange rate Blockchain is still in early-stage is searched.10 cryptocurrencies with higher levels of /Pages/Generating-Value-From- businesses. If an individual uses Bitcoin at that time. Then, a blockchain can development, and various alternative Despite higher levels of user privacy privacy (tinyurl.com/yat9hucw). In re- Big-Data-Analytics.aspx to pay for certain goods or services, the be searched for a transaction of that models and forms of cryptocurren- from Monero and Zcash, these cryp- cent years, regulators have increased 3. draglet.com. (2018). What is party with whom the transaction is be- amount and at that time. This gives away cies are evolving along with it. For tocurrencies have not yet achieved their focus on cryptocurrencies with Monero? Everything you need to ing made can know exactly how much the user’s Bitcoin address. Any other instance, to make blockchain more ap- higher popularity. For instance, as of higher degrees of nontraceability. In know. Draglet. [Online]. Available: money the individual has. This may purchases made using that address are pealing to financial institutions, the cryp- mid-July 2018, market capitalization June 2018, in testimony before the https://www.draglet.com increase the threat to personal safety. now easier to trace.4 tocurrency Zcash, which was launched of Monero and Zcash was about $2 bil- House of Representatives Committee /what-is-monero/ A supplier that has received a payment Sometimes, an act of carelessness in October 2016, has promised transac- lion and $816 million, respectively, on Financial Services Subcommittee 4. technologyreview.com. (2017, from a business would know how much on the part of the user may decrease tional privacy.6 It employs cryptogra- compared with Bitcoin’s $115 billion and on Terrorism and Illicit Finance, an Aug. 23). Bitcoin transactions money the business has. Knowing fund privacy. This happened to Ross Ul - phy to enhance user privacy. Ethereum’s $48 billion (coinmarket official of the US Secret Service rec- aren’t as anonymous as everyone availability and customer price sensi- bricht, who created the online black Zcash transactions can be made cap.com/). ommended better regulation of less hoped. Technology Review. [On- tivity could affect future negotiations. market Silk Road, best known as a plat- transparent, like those of Bitcoin, or traceable cryptocurrencies to pre - line]. Available: https://www Finally, if online businesses have infor- form for selling illegal drugs. When shielded through a zero-knowledge REGULATORY AND LAW vent illegal activities from benefiting .technologyreview.com/s/608716 mation about a consumer’s spending Ulbricht looked for help to expand the proof. Zcash transactions have two ENFORCEMENT RESPONSES from nontraceable coins (tinyurl.com/ /bitcoin-transactions-arent- patterns, they could predict the high- Silk Road business, he used the same types of addresses: transparent and Regulatory and law enforcement agen- ycot283t). as-anonymous-as-everyone-hoped/ est price that the consumer could pay. pseudonym that he had adopted pre- shielded. In transparent addresses, as cies are now focusing on illegal activi - 5. E. Aldaz-Carroll and E. Aldaz- The business could then use price tam- viously to post announcements on ille- is the case for Bitcoin, the monetary ties associated with cryptocurrencies. Carroll. (2018, Feb. 1). Can pering to increase profits.3 gal drug discussion forums. This made amount of the transaction as well as Law enforcement agencies are con- ryptocurrencies’ transparency cryptocurrencies and blockchain him an FBI suspect. The FBI tracked information about the receiver and the cerned with the anonymity features of and immutability features help fight corruption? Brookings. UNWANTED PERSONAL his IP address to an Internet café in sender appears in the blockchain. On cryptocurrencies. At a congressional come with a privacy cost. Ad- [Online]. Available: https:// INFORMATION LEAKS San Francisco and caught Ulbricht as the other hand, if a shielded address is hearing, former assistant US attorney Cversaries can use a range of actions to www.brookings.edu/blog/future- There are various sources of unwanted he was logging in to Silk Road as an used, the address is “obscured” on the Kathryn Haun noted that, when regu- identify the actual user associated with development/2018/02/01 personal information leaks in transac- administrator.1 public ledger. Also, if both the sender lators issue subpoenas requesting doc- a specific cryptocurrency account. /can-cryptocurrencies-an tions involving cryptocurrencies. For Another privacy problem oc - and the receiver use shielded addresses, uments relating individual identities It is important for cryptocurrency d-blockchain-help-fight-corruption/ instance, while Bitcoin transactions curs when users of cryptocurrencies the transaction amount is encrypted. to illicit activities at cryptocurrency users to be aware that their privacy can 6. L. Clozel. (2016). How Zcash tries to are difficult to track, they are not- com such as Bitcoin reuse addresses. By Users of shielded addresses con- exchanges, subpoenas may return in- be compromised. Users need to take balance privacy, transparency in pletely anonymous. All transactions are doing so, they publicly disclose infor- stitute a small proportion of Zcash formation such as “Mickey Mouse” liv- precautions to minimize privacy vio- Blockchain. American Banker. [On- recorded in a permanent public ledger. mation about past financial transac- adopters. In early 2017, shielded ad - ing at “123 Main Street” (tinyurl.com/ lations and mitigate the risk of privacy line]. Available: http://www After the Bitcoins are moved from that tions, and this can compromise their dresses accounted for about 0.8% of y8g2x23c). breaches. Users should refrain from re- .americanbanker.com/news address, financial movements can be privacy. The transparency and im- Zcash transactions.7 That propor- Academic researchers and block- using identities in both their noncryp- /law-regulation/how-zcash-tries- traced. Users can be traced through IP mutability features of cryptocur - tion is predicted to increase to 4% by chain intelligence companies are us- tocurrency and cryptocurrency worlds. to-balance-privacy-transparency- addresses and money flows. A team of rencies like Bitcoin make it possible mid-2018.8 ing advances in computer science, Likewise, by reusing cryptocurrency in-blockchain-1092198-1.html researchers studied 130 major merchants to track every transaction involving A relatively low adoption rate of economics, and forensics to help law addresses, users are more likely to pub- 7. A. Hertig. (2017, Jan. 13). Hardly that allow Bitcoin transactions. They a given address. Even if a person has shielded addresses might be due to the enforcement. Law enforcement agen- licly disclose personal information. anyone seems to be using Zcash’s found that at least 53 of the merchants engaged in careful processes to hide additional time and computational re- cies now have access to advanced tech- Higher levels of privacy require generat- anonymity features. Coin Desk. leaked payment information to at least 40 his or her identity, once a link has sources required. Shielded addresses niques to track illegal activities that ing a new address for each transaction. [Online]. Available: https://www third parties. While most of the informa- been established between a person’s require a more computationally in- employ cryptocurrencies. Elliptic, .coindesk.com/hardly-anyone-is- tion leaked was intentional and used for identity and a Bitcoin address, all past tensive process. To use Zcash’s pri- a blockchain intelligence company, ACKNOWLEDGMENTS using-zcashs-anonymity-features- advertising and analytics, some merchant transactions made by the owner of the vacy features, users may need 4 GB or uses artificial intelligence to scan I thank Jeff Voas for numerous edits but-we-couldnt-tell-if-they-were/ websites also leaked precise blockchain Bitcoin address will be associated to more of RAM (tinyurl.com/y9dtj3dh). and analyze the Bitcoin network to and suggestions on previous versions 8. B. Penny. (2018, May 3). What is ZEC? transaction information to trackers.4 the owner’s identity. With 4 GB of RAM, operations were identify suspicious transactions. It of this article. Introduction to Zcash: Blockchains

www.computer.org/computingedge 29 100 COMPUTER WWW.COMPUTER.ORG/COMPUTER NOVEMBER 2018 101 can cause zzzzz, but pure currency able: https://blog.z.cash cryptos can really push boundaries. /software-usability-and-hardware- NIR KSHETRI is a professor of Crypto Briefing. [Online]. Available: requirements/ management in the Bryan School of Business and Economics at the Uni - https://cryptobriefing.com 10. A. Greenberg. (2018, Mar. 27). versity of North Carolina at Greensboro. /what-is-zec-introduction-to-zcash/ The dark web’s favorite currency Contact him at [email protected]. 9. P. Peterson. (2016, Oct. 19). User is less untraceable than it seems. expectations at Sprout Pt. 2: Wired. [Online]. Available: Software usability and hardware https://www.wired.com/story This article originally appeared in requirements. Zcash. [Online]. Avail- /monero-privacy/ Computer, vol. 51, no. 11, 2018.

Call for Papers: IEEE Transactions on Computers

Publish your work in the IEEE Computer Society’s flagship journal, IEEE Transactions on Computers. The journal seeks papers on everything from computer architecture and software systems to machine learning and quantum computing.

Learn about calls for papers and submission details at www.computer.org/tc.

30 ComputingEdge January 2020 NOVEMBER 2018 111 20mitp06-andriole-2876926.3d (Style 4) 12-10-2019 14:50

COLUMN: LIFE IN THE C-SUITE

Skills and Competencies for Digital Transformation

Stephen J. Andriole Digital transformation requires a special set of skills and Villanova University School competencies, such as BPM, robotic process automation, of Business cloud computing, emerging technology, agile program Editor: Stephen J. Andriole, management, cybersecurity, and effective internal and Villanova University School external communications skills. of Business; [email protected]

CIOs, CTOs, CMOs, COOs, CFOs, and CEOs need teams with the right skills and competencies, especially ones that enable digital transformation. At the very least, teams need skills and competencies in the following areas.

BUSINESS ANALYSIS MODELING, SIMULATION, AND AUTOMATION The requirement here includes knowledge of business process modeling/management (BPM), robotic process automation (RPA), requirements identiﬁcation, modeling and validation, and, of course, digital transformation itself. It also assumes the ability to model, simulate and measure existing and future business processes and whole new business models. This area also assumes knowledge of, and experience with, requirements matching with external vendor capabilities and speciﬁc transformation programs and projects.

EMERGING TECHNOLOGIES, ESPECIALLY DISRUPTIVE TECHNOLOGIES This requirement includes knowledge of emerging technologies that might—and should—disrupt the business rules, processes, and models of speciﬁc vertical industries and companies. The requirement also assumes competency in competitive technology intelligence. It assumes wide and deep knowledge of, and experience with, the adoption of disruptive technology. Of special importance are emerging/disruptive technologies like virtual/augmented reality, automated reasoning, distributed ledger technology, cashless payment systems, real-time statistical/augmented analytics, simulation/ gaming technology, location-based technology, and disruptive interface technologies like intelligent speech and facial recognition, among others.

IT Professional Published by the IEEE Computer Society 2469-7087/20November/December © 2020 IEEE 2018 Published by the IEEE Comput78er Society January1520-9202/18/$33.00 2020 ß2018 IEEE31 20mitp06-andriole-2876926.3d (Style 4) 12-10-2019 14:50

LIFE IN THE C-SUITE

STATISTICAL AND AUGMENTED ANALYTICS The requirement here includes knowledge of structured and unstructured descriptive, explanatory, and predictive analytics. It also includes knowledge of, and experience with, the major open source analytics platforms like Hadoop and Spark, among others. It focuses on data science, data representation, deep learning, simulation, and displays. The requirement also includes knowledge of augmented analytics, which, according to the Gartner Group, is “an approach that automates insights using machine learning and natural-language generation, (and) marks the next wave of disruption in the data and analytics market.”1

CLOUD COMPUTING The requirement here includes knowledge of all ﬂavors of cloud delivery, including all of the service models that cloud computing provides, such as infrastructure (IaaS), software (SaaS), platform (PaaS), security (SaaS), mobility (MaaS), analytics (AaaS), blockchain (BaaS), and even learning (LaaS). It is also about knowledge of, and experience with, alternative cloud delivery architectures, cloud service level agreements (SLAs), cloud performance metrics, and cloud virtualization (especially container) technologies.

PERFORMANCE METRICS The requirement here is on operational, delivery, organization, and ﬁnancial metrics, including metrics around online cloud application performance, cloud application availability, delivery incidents, SLA adherence, project performance (especially satisfaction), personnel performance reviews, budgeting, and resource costs. Knowledge and experience here also refer to the tools available to track, measure, and report technology performance metrics.

REMOTE, AGILE PROJECT, AND PROGRAM MANAGEMENT This requirement includes knowledge of project and program management tools, techniques, and best practices. It assumes knowledge of, and experience with, project and program management of small and large-scale technology projects and familiarity with the array of tools available to the professional project and program managers. This assumes the ability to manage projects and programs cost- effectively, within task-deﬁned timelines, and remotely. It also assumes agility.

COMPETITIVE VENDOR MANAGEMENT This requirement includes knowledge of technology vendor management best (and worst) practices. This assumes knowledge of, and experience with, the development of requests for information, requests for proposals, and requests for quotes, including automated tools to develop and compare these documents. This also assumes the development of detailed SLAs and the management tools for measuring SLA compliance and performance. Communications and negotiation skills are also part of this skills/competencies area.

DIGITAL SECURITY AND SECURITY MANAGEMENT The requirement here includes knowledge of the variety of current and emerging security technologies including blockchain technology, multifactor authentication, application isolation, intelligent/automated security tools, mobile application wrapper technology, detection technologies, IaaS/SaaS device security technologies, automated testing, and pervasive/IoT security technologies, among others. The security requirement also includes knowledge of security challenges and processes, including security policies and the adoption of best practices, compliance with industry standards (such as ISO27002), regulatory compliance (such as with

32November/December 2018ComputingEdge 79 www.computer.org/itproJanuary 2020 20mitp06-andriole-2876926.3d (Style 4) 12-10-2019 14:50 20mitp06-andriole-2876926.3d (Style 4) 12-10-2019 14:50

LIFE IN THE C-SUITE IT PROFESSIONAL

STATISTICAL AND AUGMENTED ANALYTICS GDPR), vulnerability assessment/remediation, penetration testing, incident response, network and systems monitoring, forensic analysis, security awareness and training, backup, and recovery, The requirement here includes knowledge of structured and unstructured descriptive, explanatory, and among others. The focus should be on audit-approved security-as-a-service, not on in-house predictive analytics. It also includes knowledge of, and experience with, the major open source security delivery core competencies. analytics platforms like Hadoop and Spark, among others. It focuses on data science, data representation, deep learning, simulation, and displays. The requirement also includes knowledge of augmented analytics, which, according to the Gartner Group, is “an approach that automates insights INTERNAL AND EXTERNAL COMMUNICATIONS SKILLS using machine learning and natural-language generation, (and) marks the next wave of disruption in the data and analytics market.”1 This requirement includes experience writing reports and creating presentations that are easily understood and therefore actionable. The key to communication is purposeful brevity: Is the team capable of such (written and oral) communication? Communications should also be CLOUD COMPUTING customized to specific audiences, such as executives, boards, internal auditors, sales and marketing professionals, and customers, among others. Making presentations that are easily The requirement here includes knowledge of all flavors of cloud delivery, including all of the service understood and therefore actionable is an essential skill. Verbal communications should also be models that cloud computing provides, such as infrastructure (IaaS), software (SaaS), platform (PaaS), customized to specific audiences, such as executives, boards, internal auditors, sales and security (SaaS), mobility (MaaS), analytics (AaaS), blockchain (BaaS), and even learning (LaaS). It is marketing professionals, and customers, among others. The requirement also includes also about knowledge of, and experience with, alternative cloud delivery architectures, cloud service experience presenting to outside constituencies and stakeholders, especially vendors, external level agreements (SLAs), cloud performance metrics, and cloud virtualization (especially container) auditors, customers, and professional organizations. Senior members of the technology team technologies. must be “presentable” to a wide external audience. As the company’s business-technology representatives—and as one of the principal spokespersons for digital transformation—the PERFORMANCE METRICS senior team (especially the CIO, CTO, CMO, COO, CFO, and CEO) must all be superb presenters. The requirement here is on operational, delivery, organization, and financial metrics, including metrics around online cloud application performance, cloud application availability, delivery incidents, SLA adherence, project performance (especially satisfaction), personnel performance reviews, budgeting, FILLING THE GAPS and resource costs. Knowledge and experience here also refer to the tools available to track, measure, If a company is not a and report technology performance metrics. These skills and competencies should be used to assess digital transformation capabilities, which involves an objective disruptor, it is disruptable. workforce assessment of the business-technology team. If gaps REMOTE, AGILE PROJECT, AND exist—as they likely will—CIOs, CTOs, CMOs, COOs, CFOs, Digital transformation thus PROGRAM MANAGEMENT and CEOs must react accordingly. Digital transformation is becomes a survival tactic complicated yet potentially extremely impactful, especially This requirement includes knowledge of project and program management tools, techniques, and best when transformation leverages emerging and disruptive and a long-term strategy. practices. It assumes knowledge of, and experience with, project and program management of small technology, but skills and competencies gaps must be and large-scale technology projects and familiarity with the array of tools available to the professional addressed. project and program managers. This assumes the ability to manage projects and programs cost- CIOs, CTOs, CMOs, COOs, CFOs, and CEOs have three options: repair, rent, or replace. The effectively, within task-defined timelines, and remotely. It also assumes agility. repair option is often a good one: retrain and retool the willing keepers. Rethink how many full- time permanent technology professionals are necessary: rent the others as consultants, COMPETITIVE VENDOR MANAGEMENT contractors, and long-term vendors. Unfortunately, companies may also have to replace some members of the business-technology team. While this is always difficult, unsalvageable talent This requirement includes knowledge of technology vendor management best (and worst) practices. threatens competitiveness. This assumes knowledge of, and experience with, the development of requests for information, requests for proposals, and requests for quotes, including automated tools to develop and compare Digital transformation is challenging—and continuously necessary. This is not the first time we have these documents. This also assumes the development of detailed SLAs and the management tools for heeded the call to “re-engineer,” and it will not be the last. “Digital transformation” is today’s unique measuring SLA compliance and performance. Communications and negotiation skills are also part of call to action. It is unique today because of the trajectory of digital technology and the impact that this skills/competencies area. current, emerging, and disruptive technology has had on business processes and whole new business models. Industries and companies now live in fear of disruption because of what is happened to the travel, delivery, transportation, insurance, and retail industries. The real estate, banking, and election DIGITAL SECURITY AND SECURITY MANAGEMENT industries are next—and with a vengeance. The requirement here includes knowledge of the variety of current and emerging security Said differently, if a company is not a disruptor, it is disruptable. Digital transformation thus technologies including blockchain technology, multifactor authentication, application isolation, becomes a survival tactic and a long-term strategy.CIOs,CTOs,CMOs,COOs,CFOs,and intelligent/automated security tools, mobile application wrapper technology, detection CEOs have little choice. They must identify the skills and competencies necessary to remain technologies, IaaS/SaaS device security technologies, automated testing, and pervasive/IoT competitive. The list will change over time, and sometimes very quickly—disruptively. C- security technologies, among others. The security requirement also includes knowledge of Suiters must educate, re-educate, train, retrain, replace, and rent the necessary skills and security challenges and processes, including security policies and the adoption of best practices, competencies quickly, effectively, and continuously to assure competitiveness through digital compliance with industry standards (such as ISO27002), regulatory compliance (such as with transformation.

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LIFE IN THE C-SUITE

REFERENCE 1. “Augmented analytics is the future of data and analytics,” Gartner, Jul. 2017. Available at: https://www.gartner.com/doc/3773164/augmented-analytics-future-data-analytics

ABOUT THE AUTHOR

Stephen J. Andriole is the Thomas G. Labrecque Professor of Business Technology with the Villanova School of Business, Villanova University, where he teaches courses in strategic technology and innovation and entrepreneurialism. Contact him at [email protected].

This article originally appeared in IT Professional, vol. 20, no. 6, 2018.

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Ubiquitous Requirements Engineering

A Paradigm Shift That Affects Everyone

Karina Villela, Eduard C. Groen, and Joerg Doerr

IN RECENT YEARS, we have wit- thereby become ubiquitous.1 Our In this department, we discuss nessed profound changes in busi- view, as shown in Figure 1, consists four of the six transformations to- ness and society. The use of digital of six dimensions of ubiquity in ward ubiquitous RE, combining technologies has brought about dis- RE. For each dimension, we have “open RE” and “cross-domain RE” ruptive changes in every domain, identified the transformation (col- due to their strong synergy. These changes that are widely known as the ored rectangle) required to overcome transformations have a great impact “digital transformation.” Systems are the critical barrier posed by the sta- in industry and may have imminent growing increasingly interconnected tus quo (gray rectangle) for the way implications for your work prac- and complex with cyberphysical sys- RE is performed in the digital trans- tice. We will begin each section by tems even sensing and actuating in formation era. describing what has changed in the the physical world. Typical computer, You have probably noticed a shift world and how RE needs to adapt. tablet, and smartphone users include in how your company is doing busi- Then we will paint a picture of how anyone from children to the elderly. ness. Some companies experience RE could function from the perspec- A single software product can now stronger dependency on other com- tive of a requirements engineer be- easily reach audiences of millions panies or have the actual need to fore we discuss the hurdles that still with unprecedented opportunities to cocreate an ecosystem with other need to be overcome. obtain feedback. companies. You may also have found The techniques that have so far that your product’s end users have Cross-Domain and Open RE to proven crucial for eliciting require- changed or that they have changed Shape Software Ecosystems ments do not hold up to the para- the way in which they communicate In all domains, we see a rapidly digm shifts that have taken place. with you or others about your prod- growing demand by companies to Consequently, we argue that require- uct. Perhaps you have worked on form partnerships with other comments engineering (RE) will have projects in which you needed to elicit panies in software ecosystems to to evolve in several dimensions and requirements from stakeholders in offer innovative digital solutions unusual ways. Any of these observa- and thereby expand their business.2

Digital Object Identi er 10.1109/MS.2018.2883876 tions may be an indication that your Through orchestrated cooperation, Date of publication: 22 February 2019 business is in need of ubiquitous RE. partners from different business

368 IEEE SOFTWAREJanuary 2020 | PUBLISHED BY THE IEEE COMPUTERPublished by the SOCIETY IEEE Comput er Society 0740-7459/19©2019IEEE2469-7087/20 © 2020 IEEE Editor: Sarah Gregory Intel Corporation REQUIREMENTS REQUIREMENTS [email protected]

Ubiquitous From Geographic From Dealing With One Collocation Domain at a Time To Worldwide Requirements RE Distribution To Dealing With Everywhere Multiple Domains From Wishing for Experienced End Engineering Users Cross- RE With Domain RE Everyone To Empowering Newbies

A Paradigm Shift That Affects Everyone Ubiquitous RE From Focusing on Karina Villela, Eduard C. Groen, and Joerg Doerr From Wishing for Software Well-Understood To Holistically Taking Processes RE for Open RE Into Consideration Everything To Accepting People, Things, and Openness Services Automated RE From Direct Interaction With Legend: Representative End Users IN RECENT YEARS, we have wit- thereby become ubiquitous.1 Our In this department, we discuss Gray Rectangles: Barriers To Indirect Interaction With a nessed profound changes in busi- view, as shown in Figure 1, consists four of the six transformations to- Colored Boxes: Required Transformations Circles: Dimensions of Ubiquity Crowd ness and society. The use of digital of six dimensions of ubiquity in ward ubiquitous RE, combining technologies has brought about dis- RE. For each dimension, we have “open RE” and “cross-domain RE” ruptive changes in every domain, identified the transformation (col- due to their strong synergy. These FIGURE 1. The six dimensions of RE ubiquity.1 changes that are widely known as the ored rectangle) required to overcome transformations have a great impact “digital transformation.” Systems are the critical barrier posed by the sta- in industry and may have imminent growing increasingly interconnected tus quo (gray rectangle) for the way implications for your work prac- sectors and different domains can or at least their contributions to the the inherent openness of software and complex with cyberphysical sys- RE is performed in the digital trans- tice. We will begin each section by provide high-level services that go ecosystem may still be unclear. ecosystems. Requirement engineers tems even sensing and actuating in formation era. describing what has changed in the far beyond their current and in- Requirements engineers have used must be capable of fostering the si- the physical world. Typical computer, You have probably noticed a shift world and how RE needs to adapt. dividual offerings. New business glossaries, domain concepts, and multaneous shaping of business and tablet, and smartphone users include in how your company is doing busi- Then we will paint a picture of how models and processes arise in sce- domain-relevant processes to famil- software and be able to deal with un- anyone from children to the elderly. ness. Some companies experience RE could function from the perspec- narios where business and techni- iarize themselves with new business certainty. The skills of requirements A single software product can now stronger dependency on other com- tive of a requirements engineer be- cal solutions influence each other domains and facilitate a shared un- engineers need to shift from being easily reach audiences of millions panies or have the actual need to fore we discuss the hurdles that still and therefore must be shaped at the derstanding among project stake- able to elicit and represent knowledge with unprecedented opportunities to cocreate an ecosystem with other need to be overcome. same time. A good example is the holders. Some have been switching and requirements obtained from do- obtain feedback. companies. You may also have found agricultural domain, which is being among domains rather than special- main experts to being able to connect The techniques that have so far that your product’s end users have Cross-Domain and Open RE to influenced by technologies based on izing in one domain or subdomain. businesses and propose requirements proven crucial for eliciting require- changed or that they have changed Shape Software Ecosystems the Internet of Things and big data These practices can help with the to domain experts. In this sense, a re- ments do not hold up to the para- the way in which they communicate In all domains, we see a rapidly and where the interplay of farming shaping of cross-domain ecosystems, quirement engineer acts instead as a digm shifts that have taken place. with you or others about your prod- growing demand by companies to equipment manufacturers, chemical but they do not suffice; requirements business transformer.3 Consequently, we argue that require- uct. Perhaps you have worked on form partnerships with other com- industry, insurance companies, and engineers must be capable of fostering Cross-domain and open RE starts ments engineering (RE) will have projects in which you needed to elicit panies in software ecosystems to farm management providers creates connections among several business with the identification of key eco- to evolve in several dimensions and requirements from stakeholders in offer innovative digital solutions new cross-domain software ecosys- domains. In a similar way, adopting system partners, which are concrete unusual ways. Any of these observa- and thereby expand their business.2 tems. For planned and even for ex- an incremental life cycle or an agile organizations interested in being

Digital Object Identi er 10.1109/MS.2018.2883876 tions may be an indication that your Through orchestrated cooperation, isting software ecosystems, several development approach can help but part of the software ecosystem. A Date of publication: 22 February 2019 business is in need of ubiquitous RE. partners from different business partners might still be unknown, does not suffice when dealing with requirements engineer is part of an

ecosystem leadership team, which partners, 2) models and visualiza- of motivational techniques to boost also includes an integration archi- tions to provide different ecosystem the generation of user feedback.5 tect, an experience designer, and views on different levels and from Together, these approaches address others. This team seeks to make the different perspectives, and 3) means typical problems experienced in RE, design of the software ecosystem as to support a continuous change pro- including engaging a high number tangible as possible. As a business cess based on runtime monitoring of of stakeholders, prioritizing require- transformer, the requirements en- emergent behavior. ments reliably, and refining coarse- gineer plans workshops where the grained requirements. key partners play with alternative Automated RE When automation is employed in physical representations of the eco- to Exploit User Feedback RE, the role of the requirements engi- system to find out how business Software is a commodity for virtually neer resembles that of a data analyst. flows can take better advantage of everyone. There is hardly any business No direct interaction with end us- the assets of the ecosystem partners area that is devoid of any software ers takes place to elicit requirements. and ensure benefits to the overall support whatsoever. On the flip side Rather, the requirements engineer gets ecosystem. Based on their knowl- of software having become this wide- to see the results of automated analyses edge of all involved domains, the spread in both business-to-consumer conducted over user feedback, which requirements engineers also make and business-to-business settings, it should produce information about assumptions and invent require- has become hard to involve the enor- requirements, and can then make de- ments, as the domain experts do not mous pool of stakeholders, let alone cisions accordingly. A company can know yet how to transform their elicit requirements from a represen- obtain such data from all their com- business models into an innovative tative subset to build software that munication channels (e.g., social me- software ecosystem. Due to the in- meets all users’ expectations and dia, review sites, bug trackers, and herent complexity, requirements en- needs. This is especially true for a customer relationship management gineers always select a small subset heterogeneous user base, whose re- systems) and from the software prod- of the ecosystem as the scope and quirements are likely to be even more uct itself (e.g., log data, built-in feed- test their assumptions and proposed divergent. Moreover, companies have back mechanisms). Because such user requirements in short feedback cy- to deal with increasingly diverse, feedback has been shown to be a fruit- cles, either at the conceptual level or complex, and large software systems, ful source of opinions and require- by using simulation or prototypes. while the demand for fast innovation ments, text mining and usage mining During the whole process, they align calls for short feedback loops. approaches automatically extract re- the key ecosystem partners in several Traditional requirements elicita- quirements and relevant information dimensions: social, business, techni- tion techniques, such as interviews from such data. To improve the inter- cal, and legal. or focus groups, have scalability pretability and validity of the results, Currently, only a few methods problems. They stretch the limitation requirements engineers could employ and tools support the shaping of of resources when performed with crowdsourcing techniques to manu- a software ecosystem (see Villela more than a few dozen people and ally assess user feedback (e.g., rating et al.,1 Section VI.B, for a review). if they need to be performed con- or annotating sentences or validating To provide some guidance to re- tinuously to keep up with the com- analysis results). quirements engineers, we are design- petition. Besides, they are typically CrowdRE is gaining traction; ing a framework of decisions that best suited for collocated settings. practitioners are interested in the needs to be made to shape a planned Approaches for dealing with large topic, and the body of research on ecosystem together with a workflow crowds of users typically make RE automated user feedback is growing. of activities that indicate the time scalable by using new communica- However, mining techniques and for making those decisions.4 How- tion mechanisms and (big data) ana- classification algorithms have only ever, we additionally see the need lytics. We introduced the paradigm been adapted to RE recently and for 1) techniques that support the of crowd-based requirements engi- need to be further refined to pro- ideation of the ecosystem business neering (CrowdRE), which involves vide reliable results without requir- and the performance of quick vali- automated gathering and analysis ing much additional manual work. dation rounds with key ecosystem of user feedback, as well as the use What makes automatic analyses

3810 IEEE SOFTWAREComputingEdge | WWW.COMPUTER.ORG/SOFTWARE | @IEEESOFTWARE January 2020 REQUIREMENTS REQUIREMENTS

ecosystem leadership team, which partners, 2) models and visualiza- of motivational techniques to boost especially diffi cult is the inherent also includes an integration archi- tions to provide different ecosystem the generation of user feedback.5 ambiguity of unstructured user feed- tect, an experience designer, and views on different levels and from Together, these approaches address back. Moreover, companies still ne- KARINA VILLELA is a senior researcher at the Fraunhofer Insti- others. This team seeks to make the different perspectives, and 3) means typical problems experienced in RE, glect most of the communication tute for Experimental Software Engineering IESE, where she leads design of the software ecosystem as to support a continuous change pro- including engaging a high number channels they have in use, while re- the requirements engineering team. Her research interests include tangible as possible. As a business cess based on runtime monitoring of of stakeholders, prioritizing require- search has focused on public com- the trend towards ubiquitous requirements engineering, software transformer, the requirements en- emergent behavior. ments reliably, and refining coarse- munication channels. Thus, there ecosystems, and variation management. Villela received a Ph.D. gineer plans workshops where the grained requirements. is potential to assess a greater spec- in computer science from the Federal University of Rio de Janeiro. key partners play with alternative Automated RE When automation is employed in trum of feedback channels, including Contact her at [email protected]. physical representations of the eco- to Exploit User Feedback RE, the role of the requirements engi- feedback about competitor products. system to find out how business Software is a commodity for virtually neer resembles that of a data analyst. CrowdRE’s ultimate goal is not only flows can take better advantage of everyone. There is hardly any business No direct interaction with end us- to identify requirements-relevant ex- EDUARD C. GROEN is a researcher at the Fraunhofer Institute for the assets of the ecosystem partners area that is devoid of any software ers takes place to elicit requirements. pressions within user feedback but Experimental Software Engineering IESE. His research interest is and ensure benefits to the overall support whatsoever. On the flip side Rather, the requirements engineer gets also to suggest written requirements deriving requirements from natural-language texts through CrowdRE.

ecosystem. Based on their knowl- of software having become this wide- to see the results of automated analyses and perform quality checks on those AUTHORS THE ABOUT Groen received an M.S. in psychology, with a specialization in engi- edge of all involved domains, the spread in both business-to-consumer conducted over user feedback, which quasi-requirements. neering psychology, from the University of Twente and is pursuing requirements engineers also make and business-to-business settings, it should produce information about his Ph.D. in computer science at Utrecht University. Contact him at assumptions and invent require- has become hard to involve the enor- requirements, and can then make de- RE With Everyone to Support [email protected]. ments, as the domain experts do not mous pool of stakeholders, let alone cisions accordingly. A company can the Expression of Needs know yet how to transform their elicit requirements from a represen- obtain such data from all their com- or Wishes JOERG DOERR is the head of the Information Systems division at business models into an innovative tative subset to build software that munication channels (e.g., social me- Software was traditionally devel- the Fraunhofer Institute for Experimental Software Engineering IESE software ecosystem. Due to the in- meets all users’ expectations and dia, review sites, bug trackers, and oped for users who were famil- and a lecturer at the University of Kaiserslautern. His research inter- herent complexity, requirements en- needs. This is especially true for a customer relationship management iar with computers or whose tasks est is software engineering for information systems, focusing on gineers always select a small subset heterogeneous user base, whose re- systems) and from the software prod- would be supported by the software. requirements engineering, especially nonfunctional requirements. of the ecosystem as the scope and quirements are likely to be even more uct itself (e.g., log data, built-in feed- Now that digital transformation Doerr received a Ph.D. in computer science from the University of test their assumptions and proposed divergent. Moreover, companies have back mechanisms). Because such user impacts society as a whole, digital Kaiserslautern. He is a member of the German Informatics Society. requirements in short feedback cy- to deal with increasingly diverse, feedback has been shown to be a fruit- solutions affect everyone.6 Solu- Contact him at [email protected]. cles, either at the conceptual level or complex, and large software systems, ful source of opinions and require- tions designed to address societal by using simulation or prototypes. while the demand for fast innovation ments, text mining and usage mining issues, for example, in smart cities During the whole process, they align calls for short feedback loops. approaches automatically extract re- or smart rural areas, are intended the key ecosystem partners in several Traditional requirements elicita- quirements and relevant information to be used by people with different dimensions: social, business, techni- tion techniques, such as interviews from such data. To improve the inter- interests, skills, and backgrounds. cal, and legal. or focus groups, have scalability pretability and validity of the results, This includes elderly people who Currently, only a few methods problems. They stretch the limitation requirements engineers could employ have no special technological affi n- Now that digital transformation and tools support the shaping of of resources when performed with crowdsourcing techniques to manu- ity and may be hard to reach due to a software ecosystem (see Villela more than a few dozen people and ally assess user feedback (e.g., rating their fear of having digital solutions impacts society as a whole, digital et al.,1 Section VI.B, for a review). if they need to be performed con- or annotating sentences or validating forced upon them. Other settings re- solutions affect everyone. To provide some guidance to re- tinuously to keep up with the com- analysis results). quire inclusive approaches—for ex- quirements engineers, we are design- petition. Besides, they are typically CrowdRE is gaining traction; ample, when designing solutions for ing a framework of decisions that best suited for collocated settings. practitioners are interested in the people with mental or social impair- needs to be made to shape a planned Approaches for dealing with large topic, and the body of research on ments (e.g., severe forms of autism). ecosystem together with a workflow crowds of users typically make RE automated user feedback is growing. RE traditionally relies on techniques require end users who are intrinsi- needs based on their interests, skills, of activities that indicate the time scalable by using new communica- However, mining techniques and that assume stakeholders are able cally motivated and possess collab- and backgrounds. for making those decisions.4 How- tion mechanisms and (big data) ana- classification algorithms have only to express and refl ect on their re- orative skills. However, to ensure With the demand for inclusive ever, we additionally see the need lytics. We introduced the paradigm been adapted to RE recently and quirements, mostly verbally, or can the expected societal or social im- approaches, requirements engineers for 1) techniques that support the of crowd-based requirements engi- need to be further refined to pro- recognize that a particular solution pact of a digital solution, require- need to carefully plan their RE ideation of the ecosystem business neering (CrowdRE), which involves vide reliable results without requir- (e.g., a prototypical implementation) ments engineers are increasingly approach to ensure that the RE meth- and the performance of quick vali- automated gathering and analysis ing much additional manual work. meets their needs. Even more recent faced with the challenge of engaging ods fi t the end users’ characteristics. dation rounds with key ecosystem of user feedback, as well as the use What makes automatic analyses techniques, such as design thinking, end users and understanding their To do so, existing RE methods need

10 IEEE SOFTWARE | WWW.COMPUTER.ORG/SOFTWARE | @IEEESOFTWARE www.computer.org/computingedge MARCH/APRIL 2019 | IEEE SOFTWARE 1139 REQUIREMENTS

to be characterized according to as- need to be shared so others can Int. Requirements Engineering Conf. pects that are relevant for actively learn about which method is suit- (RE 18), pp. 205–216. engaging end users in RE activities, able for a particular stakeholder 2. S. Jansen, S. Brinkkemper, and M. such as duration, frequency, location, group. Overall, requirements engi- A. Cusumano, Software Ecosystems: and degree of interactivity. Require- neers need to make an effort to see Analyzing and Managing Business ments engineers can then characterize eye to eye with the stakeholders on Networks in the Software Industry, end users according to aspects, such a social level, whether by talking to Cheltenham, U.K.: Edward Elgar as their domain knowledge, attitude villagers at the market or engaging Publishing, 2013. toward IT, overall motivation, and in one-on-one sessions with a men- 3. S. Hess, J. Knodel, M. Naab, and temporal availability and select RE tally impaired person under thera- M. Trapp, “Engineering roles for methods that fi t the characteristics of peutic guidance. constructing ecosystems,” in Proc. 10th European Conference on Soft- ware Architecture Workshops, 2016, pp. 24–28. 4. K. Villela, S. Kedlaya, and J. Dörr, Overall, requirements engineers need “An approach to requirements engineering for software ecosystems,” to make an effort to see eye to eye in Proc. Requirements Engineering: with the stakeholders on a social level. Foundation for Software Quality (Essen 2019), to be published. 5. E. C. Groen et al., “The crowd in requirements engineering: The landscape and challenges,” IEEE Softw., a specifi c group of end users. Their Working Together vol. 34, no. 2, pp. 44–52. choices may lead to new methods be- To respond to the challenging de- 6. C. Ncube and S.-L. Lim, “On ing introduced, or to existing ones mands of digital transformation, RE systems of systems engineering: A being employed or adapted. will have to become ubiquitous in Requirements engineering perspective Involving end users as “cocre- several dimensions, with the role of and research agenda,” in Proc. 26th ators” of a digital solution can help the requirements engineer remain- IEEE Int. Requirements Engineer- increase participation and accep- ing central to the success of software ing. Conf. (RE 18), pp. 112–123. tance by specifi c groups. However, products. To achieve this goal, fur- 7. J. Salminen, S. Konsti-Laakso, it is necessary to offer informal set- ther applied research will be needed M. Pallot, B. Trousse, and B. Senach, tings in which they can feel comfort- to address the practical implications “Evaluating user involvement within able collaborating. The so-called and actual needs of industry and living labs through the use of a Living Lab approach7 might provide society. Practitioners are invited to domain landscape,” in Proc. 17th solutions to this challenge, but the embrace the need for change and Int. Conf. Concurrent Enterprising, motivation to actively participate in to provide insights into what they 2011, pp. 1–10. RE or to even show up still remains can contribute as well as what their a challenge. The incorporation of needs are. Only through close col- gamifi cation principles and factors laboration among research, society, This article originally appeared in that enhance motivation, such as and industry can the hurdles that IEEE Software, vol. 36, no. 2, 2019. external stimuli or incentives, so- currently still prevent true RE ubiq- cial interaction, and the assignment uity be overcome. of tasks and responsibilities, needs to be investigated. There are only a References Access all your IEEE Computer few studies that report on how they 1. K. Villela et al., “Towards ubiqui- Society subscriptions at applied cocreation and approaches tous RE: A perspective on require- computer.org such as Living Labs in settings with ments engineering in the era of digital /mysubscriptions high social impact. Such experiences transformation,” in Proc. 26th IEEE

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DEPARTMENT: IOT NEWS

The IoT and Digital Transformation: Toward the Data-Driven Enterprise

Alexander A. Pflaum Internet of Things (IoT) technologies are transforming Fraunhofer Center for Applied Research on Supply the focus of business processes from physical Chain Services SCS and products to data-driven services. The authors Otto-Friedrich University of Bamberg propose a reference process for digital transformation of the company that goes beyond traditional Philipp Gölzer Fraunhofer Center for technology-driven approaches that solely focus on Applied Research on Supply the identification, specification, and implementation of Chain Services SCS IoT solutions to also include a strategy-driven Editor: Florian Michahelles approach that takes into account complementary florian.michahelles@ technologies and innovations, considers potential siemens.com barriers to digital transformation, and develops suitable countermeasures.

Internet of Things (IoT) technologies have been with us for a while. During the last two decades, many researchers have made successful advances in smart products, communication protocols and systems, middleware and integration platforms, architectures, and applications. Scientific journals as well as management magazines profile cutting-edge IoT developments. However, less attention has been given to the IoT’s economic impact.1 This article aims to reduce this gap and give some recommendations.

THE IOT: FROM SMART PRODUCTS TO DATA- DRIVEN SERVICES IoT applications are sometimes called cyber-physical systems (CPSs). While each term has different contexts of use, we use them interchangeably here. From an “end product” point of view, both concepts feature physical goods with powerful embedded microelectronic systems that have their own identity, can sense environmental parameters, determine their position, process data,

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make their own decisions, and communicate and cooperate with the environment directly or via an “Internet of Services.”2 There is a large variety of IoT applications. Smart toothbrushes help keep your teeth and gums healthy. Smart shipping containers monitor transportation processes and protect valuable items from theft and damage. Smart machines constantly monitor their status and request maintenance before a costly breakdown. Smart vehicles such as automated guided vehicles (AGVs)—robots that autonomously move materials in a warehouse—communicate and coordinate production supplies efficiently. Although “smart” products are at the heart of IoT applications, in most cases the full applications require complementary innovations: smart products and CPSs are combined with other technologies such as cloud and mobile computing, digital social networks, and data analytics. The key insight from a management perspective is that the source of innovation does not lie within a single technology; it is the fusion of different technologies that drives innovative IoT solutions. Apps are orchestrated by combining micro services on digital platforms in the cloud and downloaded onto smartphones and other smart products. These, in turn, create data and deliver it to the web and, vice versa, use data provided by the web for their own purposes. The integration of different technologies leads to a new system enabling innovative and formerly unthinkable data-driven services.

TOWARD THE DATA-DRIVEN ENTERPRISE: A CHALLENGE FOR MANAGEMENT From an innovation management perspective, the main goal behind implementing IoT solutions is the transformation of the traditional product-oriented enterprise into its data-driven counter- part. Eventually, the comprehensive implementation of IoT solutions equates to the digitization of the company. CPSs enhance the granularity and the quality of a firm’s data pool. Once trans- lated into knowledge, data enables new service offerings and creates new turnover potential. The question is how to monetize this potential. The activities of digitization pioneers reveal two different strategies. On one hand, a company can turn a physical product into its smart equivalent, embed it into a smart service, develop a suitable business model, and sell it to the market to make additional money. On the other hand, the same company can use smart products from the market to optimize its own production processes and make them more efficient and agile. The Germany-based company Schaeffler, for instance, follows both strategies. It offers smart ball bearings that can monitor temperature, vibration, and lubrication. These smart ball bearings are then integrated into machine tools. The machine tools are in turn used to produce the smart bearings themselves. The underlying process of digital transformation is complex and has to be carefully managed.

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available in a company, and the competition for such talent is fierce—hence, quickly finding make their own decisions, and communicate and cooperate with the environment directly or via qualified staff is difficult. Finally, the maturity of digitization technologies is often either over- an “Internet of Services.”2 or understated. There is a large variety of IoT applications. Smart toothbrushes help keep your teeth and gums Digital transformation affects a firm’s strategy, its offerings, the IT infrastructure, the way to col- healthy. Smart shipping containers monitor transportation processes and protect valuable items laborate with partners, its organizational structure, overall process organization, and core compe- from theft and damage. Smart machines constantly monitor their status and request maintenance tences, as well as the overall company culture at the time.3 The potential for things going wrong before a costly breakdown. Smart vehicles such as automated guided vehicles (AGVs)—robots is therefore high. Consequently, the bottom-up approach alone is insufficient to successfully that autonomously move materials in a warehouse—communicate and coordinate production transform a company. supplies efficiently. Although “smart” products are at the heart of IoT applications, in most cases the full applications require complementary innovations: smart products and CPSs are combined Equally important is a strategy-driven or “top-down” approach that helps to avoid the difficulties with other technologies such as cloud and mobile computing, digital social networks, and data mentioned above, as well as to speed up the process. Here, the company first develops a strategic analytics. The key insight from a management perspective is that the source of innovation does vision of the data-driven version of the enterprise to identify potential barriers and to initiate not lie within a single technology; it is the fusion of different technologies that drives innovative countermeasures. For this, instruments are needed to identify, structure, and handle upcoming IoT solutions. Apps are orchestrated by combining micro services on digital platforms in the barriers that might arise during the transformation process. For example, maturity models meas- cloud and downloaded onto smartphones and other smart products. These, in turn, create data uring the degree of digital transformation3 are commonly used as a tool to determine the firm’s and deliver it to the web and, vice versa, use data provided by the web for their own purposes. position in the transformation process and to indicate potential problems and corresponding The integration of different technologies leads to a new system enabling innovative and formerly countermeasures. unthinkable data-driven services. Ultimately, the top-down and the bottom-up approaches must be combined to solve the digital transformation problem. To accomplish this, we propose the four-step iterative process shown in TOWARD THE DATA-DRIVEN ENTERPRISE: A Figure 1. CHALLENGE FOR MANAGEMENT From an innovation management perspective, the main goal behind implementing IoT solutions is the transformation of the traditional product-oriented enterprise into its data-driven counter- part. Eventually, the comprehensive implementation of IoT solutions equates to the digitization of the company. CPSs enhance the granularity and the quality of a firm’s data pool. Once trans- lated into knowledge, data enables new service offerings and creates new turnover potential. The question is how to monetize this potential. The activities of digitization pioneers reveal two different strategies. On one hand, a company can turn a physical product into its smart equivalent, embed it into a smart service, develop a suitable business model, and sell it to the market to make additional money. On the other hand, the same company can use smart products from the market to optimize its own production processes and make them more efficient and agile. The Germany-based company Schaeffler, for instance, follows both strategies. It offers smart ball bearings that can monitor temperature, vibration, and lubrication. These smart ball bearings are then integrated into machine tools. The machine tools are in turn used to produce the smart bearings themselves. The underlying process of digital transformation is complex and has to be carefully managed.

A STRUCTURED PROCESS FOR DIGITAL TRANSFORMATION Digital transformation in industry has typically followed a technology-driven “bottom-up” approach. First, innovation teams within companies are coached in all aspects of digitization technologies and their applications. These teams then identify potential use cases and work on the Figure 1. Proposed four-step reference process for digital transformation of companies. corresponding solution specifications. Next, the use cases are assessed with regard to both tech- (Source: Fraunhofer Center for Applied Research on Supply Chain Services SCS) nical feasibility and the economic benefits to the company and are ranked within an implementation roadmap. This approach requires a profound understanding of digitization technologies as well as the firm’s processes. If not all knowledge is available inside the company, external ex- The process starts with a Business Strategy step—the creation of a strategic business vision for perts can be involved. the data-driven enterprise. This vision must then be broken down into business initiatives and a set of data-driven use cases that support business strategies and goals. Setting the correct frame- One might assume that during subsequent implementation there would be no major barriers to work conditions of the IT infrastructure, partnering network, organization, human resources, in- overcome. Unfortunately, experience suggests the process is surprisingly challenging. Internal IT novation culture, and so on require a digital maturity assessment to avoid friction losses during departments, for instance, are often too focused on keeping operational systems running and implementation itself. Potential use cases must then be evaluated and prioritized as to business have problems with the innovative character of CPS-based solutions. Use cases are frequently value, data accessibility, and implementation feasibility. The most promising use case can then not implemented because cost–benefit analyses are difficult and investment in the necessary IT be implemented (“application”). infrastructure is considered too expensive. Data scientists and other specialists are not commonly

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In the next step of the digital transformation process, Knowledge Creation, a data model is first developed that includes all information necessary to solve the core problem behind the use case (“model”). This model is then populated with data coming from different sources inside and outside the company. Next, in the Knowledge Application step, companies use AI techniques to get new insights and knowledge from this data and to derive use case–specific solutions (“forecasting, optimization”). Methods and algorithms used in this context usually come from statistics, mathematics, and machine learning and must be integrated into a technical solution corresponding to the given use case. Implementation of models in applications such as a standard procurement system (SPS), a manufacturing execution system (MES), and enterprise resource planning (ERP) can create new business value for the firm. In the last step, Decision-Making Process, company management must decide how to integrate the data-driven solution into the organizational decision processes (“specification”). The vision, as well as the roadmap, can then be revised and the framework conditions adapted before the next use case is selected. Each implementation gets one step closer to the vision of the data- driven enterprise.

EFFECTS ON THE BUSINESS MODEL Our own extensive consultancy experience as well as discussions with numerous industry experts have taught us that the realization of smart products and the related digital transformation of a company will fundamentally change the company’s business model.4 The value proposition of a data-driven enterprise differs significantly: whereas previously the firm offered only a “dumb” physical product, The realization of it is now creating value from data and selling data embedded into smart products and smart services.5 The physical product recedes into the background, and the company stops being a traditional manufacturer and starts be- the related digital coming a service provider.6–7 The market side of the business model is also subject to significant changes. The product is no longer sold as an transformation of a investment good but as a service. The payment model changes from a one-off payment to a continuous cash flow based on as-a-service con- company will cepts. The market is growing because, thanks to the pay-as-a-service model, even small and medium-size companies can now afford the fundamentally formerly too-expensive good. The customer is now much more involved in the development of services, as well as in the value-creation change the process, thus fundamentally changing the character of a company’s relationship with its customers. Even the resource side of the business company’s model looks different now: the key activity is turning data into value. Digital platforms8 are needed to handle the data created and used by business model. smart products. Cost structures are changing because the firm has to establish comprehensive service processes. Additionally, organizational units focusing on the firm’s digital transformation have to be set up. And, finally, cooperation models are changing. The company has to recognize that the traditional buyer–seller relationships are disappearing and that it is part of a complex business ecosystem where companies are partners and largely cooperate at eye level.9

CONCLUSION Smart products, which are at the heart of the IoT, will drive the future digital transformation of companies and radically change their business model. The implementation of smart products and corresponding data-driven services must be carefully managed due to its game-changing character. Based on our own experience with consultancy projects as well as discussions with digitization experts, we developed an iterative reference process for digital transformation that is currently being used and evaluated in various industry and research projects carried out by the

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Fraunhofer Center for Applied Research on Supply Chain Services SCS in Nuremberg, Ger- In the next step of the digital transformation process, Knowledge Creation, a data model is first many. Of course, depending on a given company’s situation, not all of our recommendations and developed that includes all information necessary to solve the core problem behind the use case process steps might be necessary. (“model”). This model is then populated with data coming from different sources inside and outside the company. Beyond developing the process itself, we have also gained three important insights. First, it is essential that companies first create a vision for their own data-driven enterprise and then align Next, in the Knowledge Application step, companies use AI techniques to get new insights and their goals with the process’s various activities. Second, digital transformation is a race against knowledge from this data and to derive use case–specific solutions (“forecasting, optimization”). time; it is necessary to establish a digitization department as well as a supporting business eco- Methods and algorithms used in this context usually come from statistics, mathematics, and ma- system in order to move fast, avoid mistakes, and be efficient. Third, we believe that fundamen- chine learning and must be integrated into a technical solution corresponding to the given use tal change within individual enterprises as well as the industry at large can only occur with an case. Implementation of models in applications such as a standard procurement system (SPS), a open innovation culture, requiring new types of skills in both data science and service system manufacturing execution system (MES), and enterprise resource planning (ERP) can create new engineering. business value for the firm. In the last step, Decision-Making Process, company management must decide how to integrate the data-driven solution into the organizational decision processes (“specification”). The vision, as well as the roadmap, can then be revised and the framework conditions adapted before the next use case is selected. Each implementation gets one step closer to the vision of the data- REFERENCES driven enterprise. 1. M.E. Porter and J.E. Heppelmann, “How Smart, Connected Products are Transforming Competition,” Harvard Business Rev., vol. 92, no. 11, 2014, pp. 64–88. 2. C. Klötzer and A. Pflaum, “Cyber-Physical Systems as the Technical Foundation for EFFECTS ON THE BUSINESS MODEL Problem Solutions in Manufacturing, Logistics and Supply Chain Management,” Proc. 5th Int'l Conf. Internet of Things (IoT 15), 2015; doi.org/10.1109/IOT.2015.7356543. Our own extensive consultancy experience as well as discussions with numerous industry ex- 3. C. Klötzer and A. Pflaum, “Toward the Development of a Maturity Model for perts have taught us that the realization of smart products and the re- Digitalization within the Manufacturing Industry’s Supply Chain,” Proc. 50th Hawaii lated digital transformation of a company will fundamentally change Int'l Conf. System Sciences (HICSS 17), 2017, pp. 4210–4219. the company’s business model.4 4. A. Osterwalder and Y. Pigneur, Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, Wiley, 2010. The value proposition of a data-driven enterprise differs significantly: The realization of 5. H. Kagermann, “Change through Digitization—Value Creation in the Age of Industry whereas previously the firm offered only a “dumb” physical product, 4.0,” Management of Permanent Change, Springer, 2017. it is now creating value from data and selling data embedded into smart products and 6. V. Eloranta and T. Turunen, “Seeking Competitive Advantage with Service Infusion: smart services.5 The physical product recedes into the background, A Systematic Literature Review,” J. Service Management, vol. 26, no. 3, 2015, pp. and the company stops being a traditional manufacturer and starts be- the related digital 394–425. coming a service provider.6–7 The market side of the business model is 7. H. Gebauer, E. Fleisch, and T. Friedli, “Overcoming the Service Paradox in also subject to significant changes. The product is no longer sold as an transformation of a Manufacturing Companies,” European Management J., vol. 23, no. 1, 2005, pp. 14– investment good but as a service. The payment model changes from a 26. one-off payment to a continuous cash flow based on as-a-service con- company will 8. M.W. Van Alstyne, G.G. Parker, and S.P. Choundary, “Pipelines, Platforms, and the cepts. The market is growing because, thanks to the pay-as-a-service New Rules of Strategy,” Harvard Business Rev., vol. 94, no. 4, 2016, pp. 54–60. model, even small and medium-size companies can now afford the fundamentally 9. M. Papert and A. Pflaum, “Development of an Ecosystem Model for the Realization of Internet of Things (IoT) Services in Supply Chain Management—A Grounded Theory formerly too-expensive good. The customer is now much more in- Study,” Electronic Markets, vol. 27, no. 2, 2017, pp. 175–189. volved in the development of services, as well as in the value-creation change the 10. C. Frankenberger, T. Weiblen, and O. Gassmann, “Network Configuration, Customer process, thus fundamentally changing the character of a company’s re- company’s Centricity, and Performance of Open Business Models: A Solution Provider lationship with its customers. Even the resource side of the business Perspective,” Industrial Marketing Management, vol. 42, no. 5, 2013, pp. 671–682. model looks different now: the key activity is turning data into value. Digital platforms8 are needed to handle the data created and used by business model. smart products. Cost structures are changing because the firm has to establish comprehensive service processes. Additionally, organizational units focusing on the firm’s digital transformation have to be set ABOUT THE AUTHORS up. And, finally, cooperation models are changing. The company has Alexander A. Pflaum is director of the Fraunhofer Center for Applied Research on Supply to recognize that the traditional buyer–seller relationships are disappearing and that it is part of a 9 Chain Services SCS and a professor of supply-chain management at Otto-Friedrich Univer- complex business ecosystem where companies are partners and largely cooperate at eye level. sity of Bamberg. Contact him at [email protected]. Philipp Gölzer is director of the Machine Learning and Optimization Group at the Fraun- CONCLUSION hofer Center for Applied Research on Supply Chain Services SCS. Contact him at [email protected]. Smart products, which are at the heart of the IoT, will drive the future digital transformation of companies and radically change their business model. The implementation of smart products and corresponding data-driven services must be carefully managed due to its game-changing character. Based on our own experience with consultancy projects as well as discussions with digitization experts, we developed an iterative reference process for digital transformation that is This article originally appeared in currently being used and evaluated in various industry and research projects carried out by the IEEE Pervasive Computing, vol. 17, no. 1, 2018.

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Department: Internet-of-Things Editor: Amit Sheth, [email protected]

Extending Patient-Chatbot Experience with Internet-of- Things and Background Knowledge: Case Studies with Healthcare Applications

Amit Sheth Saeedeh Shekarpour Kno.e.sis-Wright State University University of Dayton Hong Yung Yip Kno.e.sis-Wright State University

& THE TRANSITION TOWARDS personalized health health conditions such as sleep apnea and heart management requires public awareness about rhythm disorder. However, to make more sense management strategies of self-monitoring, self- of IoT data, it is imperative that we develop cogni- appraisal, and self-management, eventually pav- tive approaches where they mine, interlink, ing a way to more timely interventions and higher and abstract diverse IoT data. These cognitive quality patient–clinician interactions.1 A key approaches often needs to keep the user closely enabler is patient generated health data, fueled in engaged to acquire more information, to obtain good part by the growth in wearable devices feedback, to collect verbal health conditions, and including smart watches and other Internet-of- to provide intervention and management actions. Things (IoT) for health-tracking (http://bit.ly/ The chatbot technology was initially intro- smart-wearables). These tracking devices produced as an artiﬁcial conversational agent vide “low-level” monitoring signals indicating to simulate conversations with a user using voice or text interactions (http://bit.ly/chatbot- communication).2 Its market is projected to Digital Object Identiﬁer 10.1109/MIS.2019.2905748 reach $1.23 billion by 2025 (http://bit.ly/chatbot- Date of current version 19 September 2019. market). If this technology is equipped with

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Department: Internet-of-Things Editor: Amit Sheth, [email protected]

Extending Patient-Chatbot Experience with Internet-of-

Things and Background Figure 1. A healthcare assistant bot interacts with the patient via various conversational interfaces (voice, text, and visual) to disseminate information and provide recommendation (validated by physician). The core functionalities of the chatbot (Component C in the blue box) are extended with a background HKG Knowledge: Case Studies (Component A in the green box) and an evolving PHKG (Component B in the orange box). cognitive capabilities and additionally fed by contextualization, personalization, and abstrac- with Healthcare Applications continuous stream of IoT data, it can accelerate tion1 with the use of domain-specific as well as the use of personalized health management app- patient-specific knowledge, and present examples lications with improved clinical outcomes. of three healthcare applications. Recently, the coalition of knowledge representa- Amit Sheth Saeedeh Shekarpour tion and machine learning has been the center of Kno.e.sis-Wright State University University of Dayton attention towards a more explainable cognitive CONTEXTUAL HEALTH KNOWLEDGE Hong Yung Yip computing.3,4 For a specific domain such as GRAPH AND EVOLVING Kno.e.sis-Wright State University healthcare, the chatbot technology will require PERSONALIZED KNOWLEDGE GRAPH advanced cognitive capabilities relying on the A knowledge graph is a structured representa- representation of background medical knowl- tion of all the involving concepts, relations, and edge (context) and specific health conditions of entities of a given domain. One large public knowl- & THE TRANSITION TOWARDS personalized health health conditions such as sleep apnea and heart patients (personalized knowledge). The incorpo- edge has been Web of Data that surpasses 149 bil- management requires public awareness about rhythm disorder. However, to make more sense ration of data collected from IoT and mobile lion facts collected from 9960 data sets of diverse management strategies of self-monitoring, self- of IoT data, it is imperative that we develop cogni- computing (which are often personalized data) domains (observed on October 28, 2018, at http:// appraisal, and self-management, eventually pav- tive approaches where they mine, interlink, into chatbot technology will enable constant stats.lod2.eu/). AI technologies can take advan- ing a way to more timely interventions and higher and abstract diverse IoT data. These cognitive tracking of a patient’s health condition. Further- tage of these big interlinked knowledge. In the fol- 1 quality patient–clinician interactions. A key approaches often needs to keep the user closely more, it will demonstrate the advancement lowing, we first present the motivations and then enabler is patient generated health data, fueled in engaged to acquire more information, to obtain of current conversational AI capabilities for man- discuss the two key challenges faced by current good part by the growth in wearable devices feedback, to collect verbal health conditions, and aging and mining conversations to collect evi- health systems. We describe how to augment including smart watches and other Internet-of- to provide intervention and management actions. dence about patients and generate personalized existing health strategies by extending patient- Things (IoT) for health-tracking (http://bit.ly/ The chatbot technology was initially intro- and contextualized inference complemented by chatbot experience that relies on three types of smart-wearables). These tracking devices produced as an artificial conversational agent knowledge extracted from multiple sources. input knowledge (see Figure 1): (i) a background vide “low-level” monitoring signals indicating to simulate conversations with a user using In this article, we share our perspective Health Knowledge Graph (HKG) (see Figure 1A) voice or text interactions (http://bit.ly/chatbot- on how the contemporary chatbot technology that comprises of domain and disease-specific communication).2 Its market is projected to can be extended towards a more intelligent, knowledge which may be manually developed or Digital Object Identifier 10.1109/MIS.2019.2905748 reach $1.23 billion by 2025 (http://bit.ly/chatbot- engaging, context-aware, and personalized agent. extracted from Web of Data that includes a rich Date of current version 19 September 2019. market). If this technology is equipped with Furthermore, we underline the importance of source of structured medical and life science

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Internet-of-Things

data, (ii) an evolving Patient Health Knowledge of such data in better health management is Graph (PHKG) (see Figure 1B) that incorporates likely to become more important, and chatbots Patient Generated Health Data (PGHD) from sen- can further make it easier to collect some of the sors and IoT devices and structured knowledge patient data such as symptoms or how a extracted from a patient’s Electronic Medical patient feels. Record (EMR) as well as environmental data (e.g., Contemporary implementations of chatbot pollen, air quality) from public web services. The technologies do not understand conversation PHKG continues to grow by expanding informative narrative and demonstrate very limited cogni- pieces of knowledge from continuous patient tive capabilities and commonsense reasoning. interactions with the chatbot and (iii) is refined by Handling these limitations for a broad domain healthcare provider’s feedback (see Figure 1C) on might take years, but in a specific domain such predictions and analytics. as health care, and even narrower applications, such as a specific disease, these limitations can be alleviated by extending the chatbot technol- CURRENT HEALTHCARE ogy with domain and disease-specific health CHALLENGES AND PROPOSED background knowledge (i.e., contextual and per- SOLUTIONS sonalized knowledge). There are publicly avail- Contextualization and Personalization of able generic knowledge graphs (e.g., DBpedia Patient’s Data. The first challenge for developing and Freebase) as well as healthcare-specific personal health agent is the need to contextual- knowledge source, e.g., unified medical lan- ize and personalize healthcare treatments and guage system, PubMed, systematized nomencla- decisions. Current healthcare system lacks con- ture of medicine-clinical term, and International textual and personalized knowledge about its classification of diseases. Chatbot technology 3 patients due to the limited patient–physician can acquire a context-aware (i.e., patient’s con- time spent during clinical visits, the patient’s text), domain-specific (i.e., health domain) ability to recall prior events, and clinic-centric knowledge graph (extracted and integrated system that captures only a part of relevant from external sources such as Web of Data) patient data. Contextual factors in this instance termed HKG. The HKG can be updated and syn- refer to a more in-depth health management chronized by the evolution of Web of Data or and clinical protocol knowledge that a physician relevant knowledge sources. HKG provides may utilize, whereas personalized factors essential facts (background knowledge) that are include a patient’s health history, data capturing necessary for response generation, reasoning, patients health condition (e.g., a lab or BMI), and inference components of chatbot engine. ongoing activities, and lifestyle choices. A survey The other obstacle to have a holistic overview presented in the article by Linder et al.5 reports of a patient’s circumstance is the lack of a uni- several notable barriers to the effective use of clinical decision support systems during patient fied and semantic-based approach for publishing visits, including physician losing direct eye con- and integrating an individual patient’s data. This tact with patients, falling behind schedule, gap hinders the health care system to provide a inability to type quickly enough, and feeling that comprehensive history and insight about using the computer in front of the patient is patients. To tackle this deficiency, we propose rude. It concludes that EMRs have mixed effec- to publish a knowledge graph out of anony- tiveness for supporting decision-making of mized patient data that is collected from vari- physicians since exploring them is not reason- ous sources (knowledge collected from EMR, ably agile to derive effective knowledge.4 These IoTs devices, and external web services). PHKG factors can potentially lead to missing patients’ further integrates knowledge extracted from data and likely to affect other healthcare profes- previous conversations of patients with chatbot. sionals who utilize these data. On the bright To sum up, having two background knowledge side, patients are increasingly using technology graphs (see Figure 1) to feed the core chatbot (e.g., wearables) and using mobile applications engine will enhance reasoning and prediction in to generate what is termed PGHD. Incorporation support of improving health decision making.

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data, (ii) an evolving Patient Health Knowledge of such data in better health management is LIMITED PATIENT HEALTH DATA DUE humidity, and temperature) using Foobot, an Graph (PHKG) (see Figure 1B) that incorporates likely to become more important, and chatbots TO EPISODIC VISITS AND TIME indoor air quality monitor; (c) outdoor allergens Patient Generated Health Data (PGHD) from sen- can further make it easier to collect some of the CONSTRAINTS and air quality recorded using web services sors and IoT devices and structured knowledge patient data such as symptoms or how a The American Academy of Family Physicians (ozone, pollen, and air quality); and (d) selected extracted from a patient’s Electronic Medical patient feels. (AAFP) defines primary care as promoting effective data semi-automatically (human validation with Record (EMR) as well as environmental data (e.g., Contemporary implementations of chatbot communication and encouraging the role of the strict anonymization) extracted from patient’s clin- pollen, air quality) from public web services. The technologies do not understand conversation patients as partner in healthcare. During clinical ical notes (from EMRs). A total of 110 evaluations PHKG continues to grow by expanding informative narrative and demonstrate very limited cogni- visit, the primary care physician assumes the pri- in this 150 planned completed pediatric asthma þ pieces of knowledge from continuous patient tive capabilities and commonsense reasoning. mary contact of patients for diagnosing a wide patient cohort study have been completed, each interactions with the chatbot and (iii) is refined by Handling these limitations for a broad domain range of illnesses and injuries, counselling, and lasting one or three months of participation. A healthcare provider’s feedback (see Figure 1C) on might take years, but in a specific domain such education as well as initiating preventative care. compliance rate of 89% (defined as over 75% of predictions and analytics. as health care, and even narrower applications, They are also responsible for making referrals to data requiring active patient participation) shows such as a specific disease, these limitations can specialists according to the patient’s condition. the user acceptance of such a technology. The be alleviated by extending the chatbot technol- This is a task of significant responsibility since a total number of data points collected per patient CURRENT HEALTHCARE ogy with domain and disease-specific health patient may endure prolonged suffering in case of per day is up to 1852 over 29 types of parameters. CHALLENGES AND PROPOSED background knowledge (i.e., contextual and per- a wrong referral. However, with increasing societal All data are anonymized and securely backup on SOLUTIONS sonalized knowledge). There are publicly avail- demand to healthcare resources, a significant per- the Kno.e.sis cloud. These data are integrated Contextualization and Personalization of able generic knowledge graphs (e.g., DBpedia centage of physicians reported that they ran out together using a visualization and analysis plat- Patient’s Data. The first challenge for developing and Freebase) as well as healthcare-specific of consultation time to converse and accurately form, kHealthDash (http://bit.ly/kHealthDash). personal health agent is the need to contextual- knowledge source, e.g., unified medical lan- diagnose the root cause of patients’ conditions ize and personalize healthcare treatments and guage system, PubMed, systematized nomencla- (http://bit.ly/clinical-challenges). Consequently, ARCHITECTURE OVERVIEW OF A decisions. Current healthcare system lacks con- ture of medicine-clinical term, and International some patients are being deprived of education HEALTH CHATBOT textual and personalized knowledge about its classification of diseases. Chatbot technology about their health conditions, causes, available 3 Content, user interface, and user feedback are patients due to the limited patient–physician can acquire a context-aware (i.e., patient’s con- treatments, and education (such as on lifestyle three major components that go hand-in-hand in time spent during clinical visits, the patient’s text), domain-specific (i.e., health domain) changes). This indicates a worrisome gap in col- creating a positive user experience which is a criti- ability to recall prior events, and clinic-centric knowledge graph (extracted and integrated lecting, managing and analyzing patient’s health cal for defining the relationship a user has with a system that captures only a part of relevant from external sources such as Web of Data) data as well as a proper mechanism for educating, chatbot. Having the chatbot’s core functionalities patient data. Contextual factors in this instance termed HKG. The HKG can be updated and syn- advising, and referring patients. extended with HKG and PHKG help contextualize refer to a more in-depth health management chronized by the evolution of Web of Data or Mobile devices and IoTs are increasingly preva- and clinical protocol knowledge that a physician and personalize conversations. However, without relevant knowledge sources. HKG provides lent with overall improved technology literacy may utilize, whereas personalized factors an equally strong frontend communication system essential facts (background knowledge) that are among populations. They can hence be leveraged include a patient’s health history, data capturing to (a) receive user input and (b) articulate smart necessary for response generation, reasoning, for continuous real-time tracking of patient health patients health condition (e.g., a lab or BMI), responses by (c) making intelligent inferences and and inference components of chatbot engine. signals. These signals can help in bridging the ongoing activities, and lifestyle choices. A survey prediction, user interest and experience may The other obstacle to have a holistic overview information gap between each hospital visit and presented in the article by Linder et al.5 reports decline and diminish over time (http://bit.ly/why- of a patient’s circumstance is the lack of a uni- providing just-in-time adaptive interventions.6 For several notable barriers to the effective use of chatbots-fail). The six core components of the fied and semantic-based approach for publishing example, a joint project between Kno.e.sis and clinical decision support systems during patient chatbot (see Figure 1C) each represents a and integrating an individual patient’s data. This Dayton Children’s Hospital has developed knowl- visits, including physician losing direct eye con- research problem: conversation management, nat- gap hinders the health care system to provide a edge-enabled semantic multi-sensory approach tact with patients, falling behind schedule, ural language (narrative) understanding, response comprehensive history and insight about for personalized pediatric asthma management inability to type quickly enough, and feeling that generation, knowledge extraction and discovery, patients. To tackle this deficiency, we propose (kHealth, http://bit.ly/kHealth-Asthma).7 The using the computer in front of the patient is reasoning and inference engine, and prediction to publish a knowledge graph out of anony- kHealth-Asthma kit represented in Figure 2 con- rude. It concludes that EMRs have mixed effec- module. The following are the proposed exten- mized patient data that is collected from vari- sists of an Android application that asks contex- tiveness for supporting decision-making of sions to the current state-of-the-art approaches to ous sources (knowledge collected from EMR, tual questionnaire (tailored to specific conditions physicians since exploring them is not reason- improve patient experience in using a chatbot. ably agile to derive effective knowledge.4 These IoTs devices, and external web services). PHKG of the user) to capture symptoms and medication factors can potentially lead to missing patients’ further integrates knowledge extracted from usage. It also uses IoT and Web Services to collect (a) Receive and understand user input: A chatbot data and likely to affect other healthcare profes- previous conversations of patients with chatbot. patient’s and patient relevant relevant data includ- should be sufficiently dynamic to communi- sionals who utilize these data. On the bright To sum up, having two background knowledge ing (a) physiological data captured via Fitbit (activ- cate with patients via multiple input and outside, patients are increasingly using technology graphs (see Figure 1) to feed the core chatbot ity and sleep) and Peak Flow meter (PEF/FEV1 put modalities including voice, text, and (e.g., wearables) and using mobile applications engine will enhance reasoning and prediction in values); (b) indoor environmental data (particu- smart display. The chatbot should pro- to generate what is termed PGHD. Incorporation support of improving health decision making. late matter, volatile organic compound, CO2, vide feedback to the user and affirm its

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Figure 2. The kHealth framework with kHealth-Asthma kit, kHealth cloud (D), and kHealth Dashboard (E), showing the frequency of data collection, the number of parameters collected, and the total number of data points collected per day per patient (shown in dark blue). The kHealth kit components that are given to patients which collects PGHD are shown in light blue and the outdoor environmental parameters with their sources are shown in green. All data are anonymized and associated with respective randomly assigned patient IDs.

understanding to avoid conflict and knowl- (iii) Dynamicity and evolution. The more the edge mis-representation. patient interacts with the chatbot, the more (b)Generating smart responses: The responses knowledge it discovers about the patient. In articulated by the chatbot are reasoned addition, knowledge evolves over time and from the underlying HKG and PHKG to guar- they should be reflected on the knowledge antee domain-specificity and contextualiza- bases (HKG and PHKG). tion as well as personalization aspects. (iv) Balancing response granularity and volume. The “smart” is attributed by the following The complexity of traversing the graphs components: followed by reasoning and formulating a (i) Comprehensible and concise. Conciseness response, either by visualization or verbal- and comprehensibility of answers pro- ization, increases dramatically with the foundly matter as a slight flaw could com- volume of data. Retrieving and balancing promise reliability. an optimum amount of data, yet sufficient (ii) Context-awareness and coherence. The for a reasonable response is critical to chatbot should consider the patient’s con- communicate timely and effectively. text in terms of space and time in addition (c) Inference, reasoning, and prediction. As knowl- to the input provided. For example, if an edge evolves, both HKG and PHKG should be asthmatic patient asks for the weather con- continuously updated to infer new insights dition, a generic answer would be “Today (http://bit.ly/PHKG-evolution). The prediction is fairly sunny” versus a personalized module relies on both new and historical answer with respect to the patient’s dis- knowledge about the patient in order to infer, ease “Today is fairly sunny. However, the reason, and make a reasonable recommenda- ragweed pollen is a little high which does tion to assist the patient for self-management not look too good for your health. Do and self-appraisal. The predictions are also remain indoor as much as possible.” The continuously presented to the corresponding latter illustrates context awareness. physicians to create situational awareness,

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and in case of an emergency condition, the leveraged and transformed into practical and physician can be notified immediately to actionable information for both patient and intervene. health care provider. Specifically, the patient can access information with regards to his/her asthma control level based on symptoms, sever- CASE STUDIES WITH HEALTHCARE ity, and triggers for self-monitoring, all at the APPLICATIONS convenience by conversing with the chatbot. The first use case is major depressive disor- The third prominent use case is elderly care. der. Depression is highly prevalent in the U.S. With improved healthcare services and ameni- with estimated prevalence rates of 10.5% affecting ties, elder residents are becoming one of the millions of U.S. adults (http://bit.ly/major- fastest growing cohort.9 These older adults how- depression). Successful early identification and ever are at the highest risk for developing intervention, albeit challenging, can lead to posi- chronic diseases such as heart failure and tive health and behavioral improvements. A rou- chronic obstructive pulmonary disease. As the tine screening for depression by a clinician technology matures, a chatbot with consented involves administering a Patient Health Question- access to patient–doctor profile and social infor- naire (PHQ-9, http://bit.ly/PHQ-9) which relies mation can be delegated to match patient–doc- Figure 2. The kHealth framework with kHealth-Asthma kit, kHealth cloud (D), and kHealth Dashboard (E), showing the heavily on patient’s ability to recall events that tor preferences, organize telehealth sessions, frequency of data collection, the number of parameters collected, and the total number of data points collected per day per occurred over the span of last two weeks. Instead, and schedule appointments by looking up the patient (shown in dark blue). The kHealth kit components that are given to patients which collects PGHD are shown in light a chatbot can directly converse with patient to doctor’s calendar. Extending with IoTs such as blue and the outdoor environmental parameters with their sources are shown in green. All data are anonymized and collect relevant data on a continuous basis in pill’s bottle sensor, the chatbot can be made associated with respective randomly assigned patient IDs. real-time, or as an added option, a patient can smarter to remind and nudge patient of timely consent the chatbot to use his social media con- medication intake as well as adherence to clini- versations to indirectly assess some of the com- cian prescribed management plan. By incorpo- understanding to avoid conflict and knowl- (iii) Dynamicity and evolution. The more the ponents of PHQ-9 assessment and directly rating background geospatial and gazetteers edge mis-representation. patient interacts with the chatbot, the more converse with a patient for the remaining informa- knowledge sources, it is also feasible to coordi- (b)Generating smart responses: The responses knowledge it discovers about the patient. In tion needed for an assessment. The patient’s nate and arrange transportation service for articulated by the chatbot are reasoned addition, knowledge evolves over time and PHKG can represent patient’s past encounters elderly with physical disabilities and transporta- from the underlying HKG and PHKG to guar- they should be reflected on the knowledge and behavioral manifestations (optionally on tion barriers, especially in congested cities. antee domain-specificity and contextualiza- bases (HKG and PHKG). social media) over a substantial period of time for In conclusion, while the chatbot technology tion as well as personalization aspects. (iv) Balancing response granularity and volume. a more accurate prognosis. In addition, a contex- is not new, we discussed how its potential can The “smart” is attributed by the following The complexity of traversing the graphs tualized chatbot with domain knowledge can be extended with IoTs and knowledge graphs. components: followed by reasoning and formulating a understand slang terms that are commonly used We further illustrated the possible health serv- (i) Comprehensible and concise. Conciseness response, either by visualization or verbal- in social media such as “bupe” which refers to its ices a chatbot can intervene using three disease- and comprehensibility of answers pro- ization, increases dramatically with the medical term “buprenorphine.” This allows a via- specific use cases. To sum up, the chatbot tech- foundly matter as a slight flaw could com- volume of data. Retrieving and balancing ble entry for a chatbot to deliver tailored psycho- nology can (i) be empowered with multisensory promise reliability. an optimum amount of data, yet sufficient therapy based on Cognitive-behavioral therapy8 capabilities through IoTs and sensors, (ii) pro- (ii) Context-awareness and coherence. The for a reasonable response is critical to and initiate the need for treatment intervention vide contextualized and personalized reasoning chatbot should consider the patient’s con- communicate timely and effectively. conforming to medical protocols. capabilities grounding with domain-specific text in terms of space and time in addition (c) Inference, reasoning, and prediction. As knowl- The second use case is asthma. More than knowledge, and (iii) assist situations requiring to the input provided. For example, if an edge evolves, both HKG and PHKG should be 20.4 million people in the U.S. are diagnosed high cognitive load. These diverse potentials asthmatic patient asks for the weather con- continuously updated to infer new insights with asthma in 2016 and asthma-related health- hold prodigious promising for a close future of dition, a generic answer would be “Today (http://bit.ly/PHKG-evolution). The prediction care costs alone are around $50 billion a year promoted healthcare approach. is fairly sunny” versus a personalized module relies on both new and historical (http://bit.ly/asthma-facts). In an attempt to answer with respect to the patient’s dis- knowledge about the patient in order to infer, bridge the informational gap between episodic ease “Today is fairly sunny. However, the reason, and make a reasonable recommenda- patient–doctor visits, a chatbot can combine & REFERENCES ragweed pollen is a little high which does tion to assist the patient for self-management active and passive sensing using a variety of 1. A. Sheth, U. Jaimini, and H. Y. Yip, “How will the not look too good for your health. Do and self-appraisal. The predictions are also low-cost sensors and IoTs for continuous moni- internet of things enable augmented personalized remain indoor as much as possible.” The continuously presented to the corresponding toring and collection of multimodal data. These health?,” IEEE Intell. Syst., vol. 33, no. 1, pp. 89–97, latter illustrates context awareness. physicians to create situational awareness, longitudinal measurements can then be Jan./Feb. 2018.

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2. L. Pichponreay, J. H. Kim, C. H. Choi, K. H. Lee, and symptoms of depression and anxiety using a fully W. S. Cho, “Smart answering chatbot based on OCR automated conversational agent (Woebot): A and overgenerating transformations and ranking,” in randomized controlled trial,” JMIR Mental Health, Proc. 8th Int. Conf. Ubiquitous Future Netw., pp. 1002– vol. 4, no. 2, 2017. 1005, Jul. 2016. 9. A. E. Schneider, N. Ralph, C. Olson, A. M. Flatley, and 3. A. Holzinger, C. Biemann, C. S. Pattichis, and D. B. Kell, L. Thorpe, “Predictors of senior center use among “What do we need to build explainable AI systems for older adults in New York City public housing,” J. Urban the medical domain?,” arXiv:1712.09923, 2017. Health, vol. 91, no. 6, pp. 1033–1047, 2014. 4. W. Samek, T. Wiegand, and K. R. Muller,€ “Explainable artiﬁcial intelligence: Understanding, visualizing and Amit Sheth works on semantic-cognitive-perceptual interpreting deep learning models,” arXiv:1708.08296, computing and knowledge-enhanced learning with 2017. application in healthcare and social good. He is a fellow 5. J. A. Linder, J. L. Schnipper, R. Tsurikova, A. J. Melnikas, of IEEE, AAAI, and AAAS. He is the corresponding L. A. Volk, and B. Middleton, “Barriers to electronic author of this article and can be reached at amit. health record use during patient visits,” in Proc. AMIA [email protected] and http://knoeiss.org/amit. Annu. Symp., 2006, vol. 2006, pp. 499–503. 6. I. Nahum-Shani et al., “Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support,” Ann. Behav. Med., vol. 52, no. 6, Hong Yung (Joey) Yip is currently working pp. 446–462, 2017. toward the Ph.D. degree on topics in knowledge 7. U. Jaimini, K. Thirunarayan, M. Kalra, R. Venkataraman, graph, deep learning, and conversational AI. Contact him at [email protected]. D. Kadariya, and A. Sheth, “How is my child’s asthma?” Digital phenotype and actionable insights for pediatric Saeedeh Shekarpour is an Assistant Professor of asthma, JMIR Pediatrics and Parenting, vol. 1, no. 2, computer science with the University of Dayton, Dayton, 2018, Art. no. e11988. OH, USA. She works on knowledge graphs and cogni- 8. K. K. Fitzpatrick, A. Darcy, and M. Vierhile, “Delivering tive computing in question answering and chatbot tech- cognitive behavior therapy to young adults with nologies. Contact her at [email protected].

This article originally appeared in IEEE Intelligent Systems, vol. 34, no. 4, 2019.

IEEE Intelligent Systems 52 30 ComputingEdge January 2020 CAREER OPPORTUNITIES

Assistant Professor of Computer Engineering Senior Software Engineer The University of Southern Mississippi (Sacramento, CA) IT company. The School of Computing Sciences and Computer Engineering in the College of Arts and Sciences at the University of Southern Mississippi is seeking applications for one Masters+2 yrs (Comp Science, tenure-track, assistant professor position in the fi eld of Computer Engineering with a Engineering or related fi eld) start date of fall 2020. The position will be based at the university’s main campus in Develop, create and modify Hattiesburg. general computer applications Candidates must have a Ph.D. in Computer Engineering or a closely related fi eld, software or specialized utility and be able to demonstrate the ability to develop a successful research program and programs. Analyze user needs participate effectively in the development and teaching of the Computer Engineer- and develop software solutions. ing curriculum. Applicants with a wide range of interests in Computer Engineering Design software or customize are encouraged to apply while areas of expertise related to Internet of Things, rapid software for client use with the prototyping, embedded systems, system on chip, cloud computing and, especially, aim of optimizing operational cybersecurity will be given priority consideration. e ciency. May analyze and design databases within an Applications must include: CV; cover letter; brief statement of teaching philosophy; application area, working description of research interests; and at least three references. The position will re- individually or coordinating main open until fi lled. database development as part The University of Southern Mississippi is a public, Doctoral University with Very High of a team using waterfall and Research Activity. The new Computer Engineering program started in 2017 and is Agile, Lean Six Sigma, Main frame in a rapid phase of expanding research and education activities and offers excellent Cobol, RQM, Clear Quest, JIRA, opportunities for interdisciplinary and industrial collaborations. IBM web sphere, IBM clear case. All are encouraged to visit the university and the School’s websites starting from http:// Travel and/or Relocation to www.usm.edu/computing-sciences-computer-engineering/ for general information, various unanticipated sites within and potential applicants may contact the Search Committee Chair, Dr. Amer Dawoud, the U.S. may be required. [email protected] for specifi c inquiries (Job Req # 1252). Apply with 2 copies of resume The University of Southern Mississippi is an equal employment opportunity employer. to HR, HTH Enterprise, LLC, All qualifi ed applicants will receive consideration for employment without regard to 5155 Madison Avenue, Suite 21, race, color, religion, gender, national origin, age, disability or veteran status. Sacramento, California 95841

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The IEEE Transactions on Sustainable Computing (T-SUSC ) is a peer-reviewed journal devoted to SXEOLVKLQJ KLJKTXDOLW\ SDSHUV WKDW H[SORUH WKH GLƬHUHQW DVSHFWV RI VXVWDLQDEOH FRPSXWLQJ 7KH notion of sustainability is one of the core areas in computing today and can cover a wide range of problem domains and technologies ranging from software to hardware designs to application GRPDLQV6XVWDLQDELOLW\ HJHQHUJ\HƱFLHQF\QDWXUDOUHVRXUFHVSUHVHUYDWLRQXVLQJPXOWLSOHHQHUJ\ sources) is needed in computing devices and infrastructure and has grown to be a major limitation to usability and performance.

Contributions to T-SUSCPXVWDGGUHVVVXVWDLQDELOLW\SUREOHPVLQGLƬHUHQWFRPSXWLQJDQGLQIRUPDWLRQ SURFHVVLQJ HQYLURQPHQWV DQG WHFKQRORJLHV DQG DW GLƬHUHQW OHYHOV RI WKH FRPSXWDWLRQDO SURFHVV These problems can be related to information processing, integration, utilization, aggregation, and generation. Solutions for these problems can call upon a wide range of algorithmic and computational frameworks, such as optimization, machine learning, dynamical systems, prediction and control, decision support systems, meta-heuristics, and game-theory to name a few.

T-SUSC covers pure research and applications within novel scope related to sustainable computing, such as computational devices, storage organization, data transfer, software and information SURFHVVLQJ DQG HƱFLHQW DOJRULWKPLF LQIRUPDWLRQ GLVWULEXWLRQSURFHVVLQJ $UWLFOHV GHDOLQJ ZLWK hardware/software implementations, new architectures, modeling and simulation, mathematical models and designs that target sustainable computing problems are encouraged.

SUBSCRIBE AND SUBMIT For more information on paper submission, featured articles, calls for papers, and subscription links visit: www.computer.org/tsusc Conference Calendar Questions? Contact [email protected]

EEE Computer Society conferences are valuable forums for learning on broad and dynamically I shifting topics from within the computing profession. With over 200 conferences featuring leading experts and thought leaders, we have an event that is right for you.

Find a region: Africa ■ Australia ◆ North America ◗ Asia ▲ Europe ● South America ★

February Interfaces) ◗ 3 February 23 March • ICSC (IEEE 14th Int’l Conf. on Semantic Com- • ICST (13th IEEE Conf. on Software Testing, puting) ◗ Validation and Verifi cation) ● 18 February • PerCom (IEEE Int’l Conf. on Pervasive Com- • SANER (IEEE 27th Int’l Conf. on Software Anal- puting and Communications) ◗ ysis, Evolution and Reengineering) ◗ 19 February April • BigComp (IEEE Int’l Conf. on Big Data and 5 April Smart Computing) ▲ • ISPASS (Int’l Symposium on Performance 22 February Analysis of Systems and Software) ◗ • CGO (IEEE/ACM Int’l Symposium on Code 14 April Generation and Optimization) ◗ • Pacifi cVis (IEEE Pacifi c Visualization Sympo- sium) ▲ March 20 April 2 March • ICDE (IEEE 36th Int’l Conf. on Data Eng.) ◗ • WACV (IEEE Winter Conf. on Applications of Computer Vision) ◗ May 9 March 3 May • DATE (Design, Automation & Test in Europe • FCCM (IEEE 28th Annual Int’l Symposium Conf. & Exhibition) ● on Field-Programmable Custom Computing • IRC (4th IEEE Int’l Conf. on Robotic Comput- Machines) ◗ ing) ▲ 4 May 16 March • HOST (IEEE Int’l Symposium on Hardware • ICSA (IEEE Int’l Conf. on Software Architec- Oriented Security and Trust) ◗ ture) ★ 18 May 22 March • SP (IEEE Symposium on Security and Pri- • VR (IEEE Conf. on Virtual Reality and 3D User vacy) ◗

72 January 2020 Published by the IEEE Computer Society 2469-7087/20 © 2020 IEEE • FG (IEEE Int’l Conf. on Automatic Face and September Gesture Recognition) ★ 21 September • IPDPS (IEEE Int’l Parallel and Distributed Pro- • ASE (35th IEEE/ACM Int’l Conf. on Automated cessing Symposium) ◗ Software Eng.) ◆ 23 May 28 September • ICSE (IEEE/ACM 42nd Int’l Conf. on Software • ICSME (IEEE Int’l Conf. on Software Mainte- Eng.) ▲ nance and Evolution) ◆ 30 May • SecDev (IEEE Secure Development) ◗ • ISCA (ACM/IEEE 47th Annual Int’l Sympo- sium on Computer Architecture) ● October 18 October June • MODELS (ACM/IEEE 23rd Int’l Conf. on Model 14 June Driven Eng. Languages and Systems) ◗ • CVPR (IEEE Conf. on Computer Vision and 21 October Pattern Analysis) ◗ • FIE (IEEE Frontiers in Education Conf.) ● 16 June 25 October • EuroS&P (IEEE European Symposium on • VIS (IEEE Visualization Conf.) ◗ Security & Privacy) ● 19 June November • JCDL (ACM/IEEE Joint Conf. on Digital Librar- 9 November ies) ▲ • FOCS (IEEE 61st Annual Symposium on Foun- 29 June dations of Computer Science) ◗ • DSN (50th Annual IEEE/IFIP Int’l Conf. on 15 November Dependable Systems and Networks) ● • SC ◗ 30 June 16 November • MDM (21st IEEE Int’l Conf. on Mobile Data • LCN (2020 IEEE 45th Conf. on Local Computer Management) ● Networks) ◆

July December 6 July 10 December • ICME (IEEE Int’l Conf. on Multimedia and • AIKE (IEEE Third Int’l Conf. on Artificial Intel- Expo) ● ligence and Knowledge Eng.) ◗ 8 July • ICDCS (IEEE 40th Int’l Conf. on Distributed Computing Systems) ▲ 13 July • COMPSAC (IEEE Annual Computer Software and Applications Conference) ● Learn more about August 31 August IEEE Computer • RE (IEEE 28th Int’l Requirements Eng. Conf.) Society Conferences ● www.computer.org/conferences

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