DOCSLIB.ORG

Using Triples As the Data Model for Blockchain Systems

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Using Triples As the Data Model for Blockchain Systems IPVS/AS Using Triples as the Data Model for Blockchain Systems Dennis Przytarski Agenda • Motivation • Our approach • Evaluation • Example Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 2 Motivation Blockchain Systems • Initially designed for cryptocurrencies • Simple and fixed data model • Other scenarios • Automotive • Full history of a vehicle • Real Estate • Record of land titles • Voting • Reduce voter fraud Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 3 Motivation Blockchain Systems latest application state • Key-value data model World State • Simple query engine queries Query Key-value store • World state Engine (Document store) • Scenarios is derived from • Transportation/Trucking Blockchain • Tracking journey stops, parcel service Ledger Block n-2 Block n-1 Block n • Supply Chain Integrity Header Header Header • Food chain, waste management TRXN TRXN TRXN ... ... ... • Requirements • Flexible information model • Query engine for Transaction • World state Key Value • History (analytics, audit trails) key value ... ... based on [1] CAR0 {color:“blue“,make:“Ford“} Example Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 4 Merge Blockchain and Database Systems BLOCKCHAIN SYSTEMS DATABASE SYSTEMS • immutability • generic but flexible data model • tamper-resistance • powerful query engine DESIGN REQUIREMENTS • maintain a flexible information model and an efficient data representation • support a powerful query engine • preserve the integrity of the blockchain‘s data structure (tamper-resistance) Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 5 Data Model • Key-value • values are often serialized • if key is not known, a full scan is necessary • no powerful query engine, just get/set operations • Relational • too strict for immutable data • schemas evolve over time leading to schema changes • Triples • flexible schemas without maintaining an one-size-fits-all schema • triples are facts data of interest are easier to extract Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 6 Merkle B-Tree Hash A Root node … Pointer Hash B Pointer Hash C Pointer Hash D Node Data Data Data Hash B Hash C Hash D’ • Pointer: Memory address • Hash: Hash over the node’s data [2]: The Merkle B-Tree: Li, Feifei, et al. "Dynamic authenticated index structures for outsourced databases." SIGMOD 2006. Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 7 Architecture latest application state World State queries Query Query Key-value store Engine Engine (Document store) queries queries historical latest application application is derived from state state Blockchain Blockchain Ledger Block n-2 Block n-1 Block n Block n-2 Block n-1 Block n Header Header Header Header Header Header TRXN TRXN TRXN ... ... ... latest application state Transaction Merkle B-tree entity attribute value Key Value <Triple> key value CAR0 car/color blue CAR0 {color:“blue“,make:“Ford“} ... ... Example CAR0 car/make Ford ... Example Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 8 Advantage (1/2) Replace three data stores by one Blockchain Block n-3 Block n-2 Block n-1 Block n Header Header Header Header TRXN TRXN TRXN TRXN ... ... ... ... Storage Architecture Analytics Blockchain World State Blockchain Transaction log Graph database Key-value store on file system Triplestore Relational database is is (Document store) exported derived from from replaced by Ledger Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 9 Advantage (2/2) Built-in Data Integrity Checks (Tamper-resistance) • The database systems ‚Analytics‘ and ‚World State‘ are not aware of the blockchain‘s data structure data integrity checks must be done manually Query Query Query Engine Engine Engine if needed if needed built-in data data integrity data integrity integrity checks checks are checks are (tamper-resistance) done manually done manually Analytics Blockchain World State Blockchain Transaction log Graph database Key-value store on file system Triplestore Relational database is is (Document store) exported derived from from Ledger Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 10 Problem • High storage requirements • Optimization mechanisms ( efficient storage techniques) • data compression • data encoding • reuse of already stored data structures Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 11 Example Transaction 1 Transaction 2 <B9F, name, „Pluto“> <B9F, type, „dwarf_planet“> <B9F, type, „planet“> Block 1 Block 2 The query engine uses Hash FC8 Hash 2F7 1 4 the Merkle B-trees of a Node AEV AEV block to compute the A <= name A <= name result of a query Hash E87 Hash FAA Hash E87 Hash C29 Nodes are 2 3 2 5 stored in a <B9F, name, „Pluto“> <B9F, type, „planet“> <B9F, name, „Pluto“> <B9F, type, „dwarf_planet“> … … … … key-value store Key Value Root of identical node after FC8 1 first block Query Block 1 E87 2 SELECT ?type ASOF n FAA 3 WHERE Query result Root of 2F7 4 [?object name „Pluto“] for n=1: ?type is planet after second block [?object type ?type] for n=2: ?type is dwarf_planet Block 2 C29 5 Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 12 Contact Details Thank you! University of Stuttgart IPVS/AS Dennis Przytarski Email: [email protected] Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 13 References • [1]: Androulaki, Elli, et al. "Hyperledger fabric: a distributed operating system for permissioned blockchains." Proceedings of the Thirteenth EuroSys Conference. ACM, 2018. • [2]: Li, Feifei, et al. "Dynamic authenticated index structures for outsourced databases." Proceedings of the 2006 ACM SIGMOD international conference on Management of data. ACM, 2006. • Merkle, Ralph C. "A digital signature based on a conventional encryption function." Conference on the theory and application of cryptographic techniques. Springer, Berlin, Heidelberg, 1987. • C. Mohan, Tutorial: Blockchains and Databases (VLDB 2017) Dennis Przytarski • Using Triples as the Data Model for Blockchain Systems 14.
Load more

Recommended publications
  • Graph Database for Collaborative Communities Rania Soussi, Marie-Aude Aufaure, Hajer Baazaoui
    Graph Database for Collaborative Communities Rania Soussi, Marie-Aude Aufaure, Hajer Baazaoui To cite this version: Rania Soussi, Marie-Aude Aufaure, Hajer Baazaoui. Graph Database for Collaborative Communities. Community-Built Databases, Springer, pp.205-234, 2011. hal-00708222 HAL Id: hal-00708222 https://hal.archives-ouvertes.fr/hal-00708222 Submitted on 14 Jun 2012 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Graph Database For collaborative Communities 1, 2 1 Rania Soussi , Marie-Aude Aufaure , Hajer Baazaoui2 1Ecole Centrale Paris, Applied Mathematics & Systems Laboratory (MAS), SAP Business Objects Academic Chair in Business Intelligence 2Riadi-GDL Laboratory, ENSI – Manouba University, Tunis Abstract Data manipulated in an enterprise context are structured data as well as un- structured data such as emails, documents, social networks, etc. Graphs are a natural way of representing and modeling such data in a unified manner (Structured, semi-structured and unstructured ones). The main advantage of such a structure relies in the dynamic aspect and the capability to represent relations, even multiple ones, between objects. Recent database research work shows a growing interest in the definition of graph models and languages to allow a natural way of handling data appearing.
    [Show full text]
  • SQL Server Protection Whitepaper
    SQL Server Protection Whitepaper Contents 1. Introduction ..................................................................................................................................... 2 Documentation .................................................................................................................................................................. 2 Licensing ............................................................................................................................................................................... 2 The benefits of using the SQL Server Add-on ....................................................................................................... 2 Requirements ...................................................................................................................................................................... 2 2. SQL Protection overview ................................................................................................................ 3 User databases ................................................................................................................................................................... 3 System databases .............................................................................................................................................................. 4 Transaction logs ................................................................................................................................................................
    [Show full text]
  • How to Conduct Transaction Log Analysis for Web Searching And
    Search Log Analysis: What is it; what’s been done; how to do it Bernard J. Jansen School of Information Sciences and Technology The Pennsylvania State University 329F IST Building University Park, Pennsylvania 16802 Email: [email protected] Abstract The use of data stored in transaction logs of Web search engines, Intranets, and Web sites can provide valuable insight into understanding the information-searching process of online searchers. This understanding can enlighten information system design, interface development, and devising the information architecture for content collections. This article presents a review and foundation for conducting Web search transaction log analysis. A methodology is outlined consisting of three stages, which are collection, preparation, and analysis. The three stages of the methodology are presented in detail with discussions of goals, metrics, and processes at each stage. Critical terms in transaction log analysis for Web searching are defined. The strengths and limitations of transaction log analysis as a research method are presented. An application to log client-side interactions that supplements transaction logs is reported on, and the application is made available for use by the research community. Suggestions are provided on ways to leverage the strengths of, while addressing the limitations of, transaction log analysis for Web searching research. Finally, a complete flat text transaction log from a commercial search engine is available as supplementary material with this manuscript. Introduction Researchers have used transaction logs for analyzing a variety of Web systems (Croft, Cook, & Wilder, 1995; Jansen, Spink, & Saracevic, 2000; Jones, Cunningham, & McNab, 1998; Wang, 1 of 42 Berry, & Yang, 2003). Web search engine companies use transaction logs (also referred to as search logs) to research searching trends and effects of system improvements (c.f., Google at http://www.google.com/press/zeitgeist.html or Yahoo! at http://buzz.yahoo.com/buzz_log/?fr=fp- buzz-morebuzz).
    [Show full text]
  • Property Graph Vs RDF Triple Store: a Comparison on Glycan Substructure Search
    RESEARCH ARTICLE Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search Davide Alocci1,2, Julien Mariethoz1, Oliver Horlacher1,2, Jerven T. Bolleman3, Matthew P. Campbell4, Frederique Lisacek1,2* 1 Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland, 2 Computer Science Department, University of Geneva, Geneva, 1227, Switzerland, 3 Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland, 4 Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia * [email protected] Abstract Resource description framework (RDF) and Property Graph databases are emerging tech- nologies that are used for storing graph-structured data. We compare these technologies OPEN ACCESS through a molecular biology use case: glycan substructure search. Glycans are branched Citation: Alocci D, Mariethoz J, Horlacher O, tree-like molecules composed of building blocks linked together by chemical bonds. The Bolleman JT, Campbell MP, Lisacek F (2015) molecular structure of a glycan can be encoded into a direct acyclic graph where each node Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search. PLoS ONE 10(12): represents a building block and each edge serves as a chemical linkage between two build- e0144578. doi:10.1371/journal.pone.0144578 ing blocks. In this context, Graph databases are possible software solutions for storing gly- Editor: Manuela Helmer-Citterich, University of can structures and Graph query languages, such as SPARQL and Cypher, can be used to Rome Tor Vergata, ITALY perform a substructure search. Glycan substructure searching is an important feature for Received: July 16, 2015 querying structure and experimental glycan databases and retrieving biologically meaning- ful data.
    [Show full text]
  • Application of Graph Databases for Static Code Analysis of Web-Applications
    Application of Graph Databases for Static Code Analysis of Web-Applications Daniil Sadyrin [0000-0001-5002-3639], Andrey Dergachev [0000-0002-1754-7120], Ivan Loginov [0000-0002-6254-6098], Iurii Korenkov [0000-0002-8948-2776], and Aglaya Ilina [0000-0003-1866-7914] ITMO University, Kronverkskiy prospekt, 49, St. Petersburg, 197101, Russia [email protected], [email protected], [email protected], [email protected], [email protected] Abstract. Graph databases offer a very flexible data model. We present the approach of static code analysis using graph databases. The main stage of the analysis algorithm is the construction of ASG (Abstract Source Graph), which represents relationships between AST (Abstract Syntax Tree) nodes. The ASG is saved to a graph database (like Neo4j) and queries to the database are made to get code properties for analysis. The approach is applied to detect and exploit Object Injection vulnerability in PHP web-applications. This vulnerability occurs when unsanitized user data enters PHP unserialize function. Successful exploitation of this vulnerability means building of “object chain”: a nested object, in the process of deserializing of it, a sequence of methods is being called leading to dangerous function call. In time of deserializing, some “magic” PHP methods (__wakeup or __destruct) are called on the object. To create the “object chain”, it’s necessary to analyze methods of classes declared in web-application, and find sequence of methods called from “magic” methods. The main idea of author’s approach is to save relationships between methods and functions in graph database and use queries to the database on Cypher language to find appropriate method calls.
    [Show full text]
  • Database Software Market: Billy Fitzsimmons +1 312 364 5112
    Equity Research Technology, Media, & Communications | Enterprise and Cloud Infrastructure March 22, 2019 Industry Report Jason Ader +1 617 235 7519 [email protected] Database Software Market: Billy Fitzsimmons +1 312 364 5112 The Long-Awaited Shake-up [email protected] Naji +1 212 245 6508 [email protected] Please refer to important disclosures on pages 70 and 71. Analyst certification is on page 70. William Blair or an affiliate does and seeks to do business with companies covered in its research reports. As a result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report. This report is not intended to provide personal investment advice. The opinions and recommendations here- in do not take into account individual client circumstances, objectives, or needs and are not intended as recommen- dations of particular securities, financial instruments, or strategies to particular clients. The recipient of this report must make its own independent decisions regarding any securities or financial instruments mentioned herein. William Blair Contents Key Findings ......................................................................................................................3 Introduction .......................................................................................................................5 Database Market History ...................................................................................................7 Market Definitions
    [Show full text]
  • GRAPH DATABASE THEORY Comparing Graph and Relational Data Models
    GRAPH DATABASE THEORY Comparing Graph and Relational Data Models Sridhar Ramachandran LambdaZen © 2015 Contents Introduction .................................................................................................................................................. 3 Relational Data Model .............................................................................................................................. 3 Graph databases ....................................................................................................................................... 3 Graph Schemas ............................................................................................................................................. 4 Selecting vertex labels .............................................................................................................................. 4 Examples of label selection ....................................................................................................................... 4 Drawing a graph schema ........................................................................................................................... 6 Summary ................................................................................................................................................... 7 Converting ER models to graph schemas...................................................................................................... 9 ER models and diagrams ..........................................................................................................................
    [Show full text]
  • Destiny® System Backups
    Destiny® system backups Establishing a backup and restore plan for Destiny Overview It is important to establish a backup and restore plan for your Destiny installation. The plan must be validated and monitored to ensure that your data is sufficiently backed up and can be recovered in the event of hardware failure or other disaster. IMPORTANT Follett recommends deploying a comprehensive backup solution and testing and monitoring all backup processes. There are tradeoff decisions to be made in the backup strategy that will be shaped by your organization’s risk tolerance, technology constraints, and ability to absorb data loss or downtime. This document provides an overview of standard backup and restore for the Destiny system. IMPORTANT This content does not cover high-availability configurations such as clustered servers or log shipping. Please contact Follett School Solutions, Inc. Technical Support should you have any questions about backing up your Destiny installation. For details, see Destiny® system backups. Backing up Destiny: an overview SQL database The heart of the Destiny data resides in the SQL database. These are the main components of SQL data to be backed up: The Destiny SQL database. This database contains the main Destiny data. Proper SQL backup configuration of this database is essential. NOTE If your installation is a consortium, you will have to ensure a proper backup of multiple SQL databases (one per member). The Destiny SQL transaction log. The Master SQL database. This system database is useful when restoring to a replacement server. It is not necessary to back up the tempdb database. This is a SQL Server work area and is recreated automatically.
    [Show full text]
  • Weaver: a High-Performance, Transactional Graph Database Based on Refinable Timestamps
    Weaver: A High-Performance, Transactional Graph Database Based on Refinable Timestamps Ayush Dubey Greg D. Hill Robert Escriva Cornell University Stanford University Cornell University Emin Gun¨ Sirer Cornell University ABSTRACT may erroneously conclude that n7 is reachable from n1, even Graph databases have become a common infrastructure com- though no such path ever existed. ponent. Yet existing systems either operate on offline snap- Providing strongly consistent queries is particularly chal- shots, provide weak consistency guarantees, or use expensive lenging for graph databases because of the unique charac- concurrency control techniques that limit performance. teristics of typical graph queries. Queries such as traversals In this paper, we introduce a new distributed graph data- often read a large portion of the graph, and consequently base, called Weaver, which enables efficient, transactional take a long time to execute. For instance, the average degree graph analyses as well as strictly serializable ACID transac- of separation in the Facebook social network is 3.5 [8], which tions on dynamic graphs. The key insight that allows Weaver implies that a breadth-first traversal that starts at a random to combine strict serializability with horizontal scalability vertex and traverses 4 hops will likely read all 1.59 billion and high performance is a novel request ordering mecha- users. On the other hand, typical key-value and relational nism called refinable timestamps. This technique couples queries are much smaller; the NewOrder transaction in the coarse-grained vector timestamps with a fine-grained timeline TPC-C benchmark [7], which comprises 45% of the frequency oracle to pay the overhead of strong consistency only when distribution, consists of 26 reads and writes on average [21].
    [Show full text]
  • Infosphere MDM Collaboration Server: Installation Guide
    IBM InfoSphere Master Data Management Collaboration Server Version 11.6 Fix Pack 15 Installation Guide IBM Note Before using this information and the product that it supports, read the information in “Notices” on page 159. Edition Notice This edition applies to version 11.6 of IBM® InfoSphere® Master Data Management and to all subsequent releases and modifications until otherwise indicated in new editions. © Copyright International Business Machines Corporation 2000, 2020. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Chapter 1. Planning to install.................................................................................1 Installation scenarios.................................................................................................................................. 1 Installation and configuration worksheets................................................................................................. 3 Installation directory worksheet............................................................................................................3 IBM Db2 data source worksheet............................................................................................................4 Oracle data source worksheet............................................................................................................... 5 WebSphere Application Server installation worksheet.........................................................................6
    [Show full text]
  • Document Vs Graph Database
    Document Vs Graph Database Is Cornelius tricentennial when Apollo reclothes telescopically? Carbonated Angelo defiling, his mainsheet ridicule paralysed mulishly. Agonistic and farinaceous Jerrold retimed her somniloquists burbles while Heinz rope some nymph tho. Recently due not the variety properties of instance data your database management systems GDBMSs have emerged as real important complement. When and augment to Choose Graph Databases over Relational. Mainly categorized into four types Key-value pair Column-oriented big-based and Document-oriented. Capture store analyze and manage collections of data. Graph databases can he thought through as a subcategory of the document store model in that they store began in documents and don't insist that data drive to a. Neo4J an sturdy and still heavily used graph paper had begun to gain. Since several of bath's data is conduct in relational database format how hot you convert any current journey to save graph database format See a. 5 Graph Databases to Consider ReadWrite. NoSQL databases or non-relational databases can be document based graph databases key-value pairs or wide-column stores NoSQL databases don't. This tape because mercury can arbitrarily decide what should rattle a vertex vs what should adorn a. Graph Databases NOSQL and Neo4j InfoQ. MongoDB and CouchDB are both examples of document stores Graph databases Last is the theater complex non-relational database will It's. An Entity Relationship Diagram commonly used to model data in SQL RDBMS vs. Column-oriented databases and document-oriented databases also belong to gauge family. Nosql Distilled was that NoSQL databases use aggregate data models than.
    [Show full text]
  • Database Management Systems Introduction Transaction ACID
    Introduction What is Concurrent Process (CP)? • Multiple users access databases and use computer Database Management systems simultaneously. • Example: Airline reservation system. Systems œ An airline reservation system is used by hundreds of travel agents and reservation clerks concurrently. Transaction, Concurrency and Why Concurrent Process? Recovery • Better transaction throughput and response time • Better utilization of resource Adapted from Lecture notes by Goldberg @ Berkeley Transaction ACID Properties of transaction • What is Transaction? • Atomicity: Transaction is either performed in its entirety or not performed at all, this should be DBMS‘ • A sequence of many actions which are responsibility considered to be one atomic unit of work. • Consistency: Transaction must take the database • Basic operations a transaction can include from one consistent state to another if it is executed in —actions“: isolation. It is user‘s responsibility to insure consistency œ Reads, writes • Isolation: Transaction should appear as though it is œ Special actions: commit, abort being executed in isolation from other transactions • Durability: Changes applied to the database by a committed transaction must persist, even if the system fail before all changes reflected on disk Concurrent Transactions Schedules • What is Schedules œ A schedule S of n transactions T1,T2,…Tn is an ordering of the B B operations of the transactions subject to the constraint that, for each transaction Ti that participates in S, the operations of Ti in Smust CPU2 A appear in the same order in which they occur in Ti. CPU A 1 œ Example: Sa: r1(A),r2(A),w1(A),w2(A), a1,c2; CPU1 T1 T2 time Read(A) Read(A) t1 t2 t1 t2 Write(A) interleaved processing parallel processing Write(A) Abort T1 Commit T2 1 Oops, something‘s wrong Another example • Reserving a seat for a flight • Problems can occur when concurrent transactions execute in an uncontrolled manner.
    [Show full text]