DOCSLIB.ORG

Process Mining on Databases: Extracting Event Data from Real-Life Data Sources

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Process Mining on Databases: Extracting Event Data from Real-Life Data Sources Process mining on databases Citation for published version (APA): Gonzalez Lopez de Murillas, E. (2019). Process mining on databases: extracting event data from real-life data sources. Technische Universiteit Eindhoven. Document status and date: Published: 27/02/2019 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal. If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement: www.tue.nl/taverne Take down policy If you believe that this document breaches copyright please contact us at: [email protected] providing details and we will investigate your claim. Download date: 09. Oct. 2021 Process Mining on Databases Extracting Event Data from Real-Life Data Sources E. González López de Murillas Process Mining on Databases: Extracting Event Data from Real-Life Data Sources E. González López de Murillas Copyright © 2019 by E. González López de Murillas. All Rights Reserved. CIP-DATA LIBRARY TECHNISCHE UNIVERSITEIT EINDHOVEN González López de Murillas, E. Process Mining on Databases: Extracting Event Data from Real-Life Data Sources by E. González López de Murillas. Eindhoven: Technische Universiteit Eindhoven, 2018. Proefschrift. A catalogue record is available from the Eindhoven University of Technology Library ISBN 978-90-386-4704-3 Keywords: Process mining, Databases, Data extraction, Case notion discovery, Case notion recommendation, Event log building, Event data querying SIKS Dissertation Series No. 2019-03 The research reported in this thesis has been carried out under the auspices of SIKS, the Dutch Research School for Information and Knowledge Systems. Process Mining on Databases: Extracting Event Data from Real-Life Data Sources PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de rector magnificus prof.dr.ir. F.P.T. Baaijens, voor een commissie aangewezen door het College voor Promoties, in het openbaar te verdedigen op woensdag 27 februari 2019 om 16:00 uur door Eduardo González López de Murillas geboren te Pamplona, Spanje Dit proefschrift is goedgekeurd door de promotoren en de samenstelling van de pro- motiecommissie is als volgt: voorzitter: prof.dr. O.J. Boxma 1e promotor: prof.dr.ir. H.A. Reijers 2e-promotor: prof.prof.h.c.dr.h.c.dr.ir. W.M.P. van der Aalst (RWTH Aachen) leden: prof.dr. A. Gal (Israel Institute of Technology) dr. M. Montali (Free University of Bozen-Bolzano) dr. G.H.L. Fletcher prof.dr. J. Ezpeleta Mateo (Universidad de Zaragoza) Het onderzoek of ontwerp dat in dit proefschrift wordt beschreven is uitgevoerd in overeenstemming met de TU/e Gedragscode Wetenschapsbeoefening. Dedicated to my little nephew, Mateo. Abstract Process mining as a discipline is gaining importance in recent years. Its focus is on the analysis of business processes by means of process execution data. It is a common misconception to think that a process mining project starts as soon as the data are available. We tend to underestimate the time, knowledge, and number of iterations needed to gather relevant data and build an event log suitable for analysis. In many cases, the data extraction and preparation phase can take up to 80% of the project duration. Improving this time-consuming initial phase will have a large impact on the whole process mining project in terms of time, cost, and quality of insights. In this thesis, we take on several challenges related to data extraction, multi- perspective event log building, and event data querying. We consider environments in which many business processes coexist, sharing data both in a centralized and distributed storage. The aim of this work is twofold. First, to ease the path for practitioners to get started with a process mining project in unknown environments when domain knowledge is not always available. Second, to provide the tools needed for event data querying and to build specialized event logs for a more effective process mining analysis. We propose several techniques that, together, constitute a pipeline connecting databases with existing process mining techniques: • A meta-model for process mining on databases (in Chapter 3). This meta-model aims to standardize the way to capture and structure both data and process aspects of an organization. • Data extraction adapters for several scenarios (in Chapter 4). These adapters, developed for some mainstream scenarios, allow us to extract and transform data from databases to comply with the proposed meta-model structure. • A technique to discover and recommend case notions in order to build event logs for process mining (in Chapter 5). This is achieved by exploring possible views on the data, correlating events by means of the underlying relations extracted from the original database, and assessing their “interestingness” with respect to some metrics. • A data-aware process oriented query language (in Chapter 7). This query lan- guage provides a compact and easy way to express relevant queries in the busi- ness context by means of constructs that exploit the underlying meta-model. The techniques presented in this thesis have been evaluated using representative sample datasets that resemble the most relevant challenges that can be found in real- viii life environments, as well as case studies with real databases. The results show that these techniques succeed at capturing a more comprehensive picture of the systems under study, improving the quality of the generated event logs, and supporting the user in the process of data extraction and log building. Moreover, they are a step ahead towards the goal of automatic data extraction and process mining analysis of complex IT systems. Implementations of our techniques are publicly available and licensed as open source. Preface Honeybees are fascinating creatures. These small insects are incredibly cooperative. They organize themselves according to a very well defined societal structure, divided mainly into reproductive and non-reproductive groups. Individually, they would not be able to survive. However, as a team they can build complicated housing structures called hives where they live, breed, store food, and protect themselves from external agents. These incredible animals do not only produce honey, but also wax, their main construction material. Both products are highly valuable for us humans due to the nutritive value of honey, and the versatility of the wax as a material in many manu- facturing processes. However, the shape in which beehives appear in nature does not allow human beekeepers to take advantage of the honey and wax production without partially or entirely destroying the hive. Nowadays, most organizations produce data as a byproduct of their business ac- tivity, similarly to the hard-working bees producing wax and honey for their survival. In many cases, the produced data is crucial for the functioning of the organization, while at the same time it acts as a record of the execution of their business processes. However, many of such organizations struggle to get the maximal potential from their data. One of the reasons is the disconnect between the data structure that supports their business processes and the data structure that would be suitable for the analysis of their process data. We believe that business process execution and analysis can coexist, sharing com- mon data sources in a sustainable manner. In order to achieve such an equilibrium, data structures need to be reshaped, similarly to the way that beekeepers provide a structure to beehives. When used responsibly, artificial beehives allow the beekeeper to benefit from the production of honey without disrupting the colony. This thesis proposes a way to structure data in order to facilitate data extraction and to enable the analysis of business processes in real-life environments. Contents List of Figures xv List of Tables xxi 1 Introduction 1 1.1 Event Data and Process Mining ........................ 2 1.1.1 Event Data ................................ 2 1.1.2 Process Mining ............................. 4 1.2 Process Mining on Databases ......................... 7 1.3 Challenges in Process Mining on Databases ................. 8 1.3.1 Challenge 1: Finding, Merging, and Cleaning Event Data .... 9 1.3.2 Challenge 2: Dealing with Complex Event Logs Having Diverse Characteristics .............................. 11 1.3.3 Challenge 3: Cross-Organizational Mining ............. 11 1.3.4 Challenge 4: Multi-Perspective Event Log Building ........ 11 1.3.5 Challenge 5: Improve Usability for Non-Experts ......... 12 1.3.6 Challenge 6: Fill the Domain Knowledge Gap in Event Log Extraction ................................ 12 1.3.7 Challenge 7: Question-Driven Log Extraction ........... 12 1.4 Contributions and Structure of this Thesis ................. 13 1.4.1 Thesis Contributions .........................
Load more

Recommended publications
  • A Data Management Life-Cycle
    science for a changing world Central Energy Resources Team Data Management A Data Management Life-Cycle Introduction Documented, reliable, and accessible data and information are essential building blocks supporting scientific research and applications that enhance society's knowledge base (fig. 1). The U.S. Geological Survey (USGS), a leading provider of science data, information, and knowledge, is uniquely positioned to integrate science and natural resource information to address societal needs. The USGS Central Energy Resources Team (USGS-CERT) provides critical information and knowledge on tnu iuu the quantity, quality, and distribution of the Nation's and the 10101010110 world's oil, gas, and coal resources. 1C001111001 By using a life-cycle model, the USGS-CERT Data Manage­ 1001001"*: ment Project is developing an integrated data management system 10^19*1- to (1) promote access to energy data and information, (2) increase data documentation, and (3) streamline product delivery to the public, scientists, and decision makers. The project incorporates web-based technology, data cataloging systems, data processing Data Information routines, and metadata documentation tools to improve data Figure 1. Access to organized and documented data and access, enhance data consistency, and increase office efficiency. information increases knowledge. Implementation Summary and Future Directions In a life-cycle model, data and information are transformed into tangible products and knowledge by a continuous flow in which The Data Management Project addresses USGS science the output of one process becomes the input of others (fig. 2). \ goals and affects how the Central Energy Resources Team manages data and carries out its programs. Our task-oriented "Find, Get, Use, Deliver, and Maintain" The tools and processes developed are based on strategy incorporates life-cycle concepts and directs USGS-CERT the life-cycle approach and our "Find, Get, Use, data management tasks and implementation.
    [Show full text]
  • Collections Management Plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library
    7 Collections Management Plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library Open-File Report 2015–1141 U.S. Department of the Interior U.S. Geological Survey Collections Management Plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library By Kelleen M. List, Brian J. Buczkowski, Linda P. McCarthy, and Alice M. Orton Open-File Report 2015–1141 U.S. Department of the Interior U.S. Geological Survey U.S. Department of the Interior SALLY JEWELL, Secretary U.S. Geological Survey Suzette M. Kimball, Acting Director U.S. Geological Survey, Reston, Virginia: 2015 For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment—visit http://www.usgs.gov/ or call 1–888–ASK–USGS (1–888–275–8747). For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprod/. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner. Suggested citation: List, K.M., Buczkowski, B.J., McCarthy, L.P., and Orton, A.M., 2015, Collections management plan for the U.S. Geological Survey Woods Hole Coastal and Marine Science Center Data Library: U.S. Geological Survey Open- File Report 2015–1141, 16 p., http://dx.doi.org/10.3133/ofr20151141.
    [Show full text]
  • Relevance of Data Mining in Digital Library
    International Journal of Future Computer and Communication, Vol. 2, No. 1, February 2013 Relevance of Data Mining in Digital Library R. N. Mishra and Aishwarya Mishra Abstract—Data mining involves significant process of Data Mining concept has been delimitated in multiple identifying the extraction of hidden predictive information ways by different organizations, scientists etc. Wikipedia, from vast array of databases and it is an authoritative new has visualized data mining a non-trivial extraction of technology with potentiality to facilitate the Libraries and implicit and potentially useful information from data and Information Centers to focus on the most important the science of extracting useful information from large data information in their data warehouses. It is a viable tool to predict future trends and behaviors in the field of library and sets or databases (http://en.wikipedia. org/wiki/Data information service for deducing proactive, knowledge-driven mining). Marketing Dictionary defines data mining as a decisions. Mechanized, prospective analyses of data mining process for extraction of customer information from a vast move beyond the analyses of past events provided by gamut of databases with the help of the feasible software to retrospective tools typical of decision support systems. Data isolate and identify previously unknown patterns or trends. Mining, Process of Data Mining, Knowledge Discovery in Multiple techniques with the help of technologies are Databases, DBMS, Data Mining Techniques etc. etc. have been discussed in this paper. employed in data mining for extraction of data from the heave of databases. Intelligence Encyclopedia has, however, Index Terms—Data mining, KDD, artificial neural Networks, defined data mining as a statistical analysis technique to sequential pattern, modeling, DMT.
    [Show full text]
  • Best Practices Using Base SAS Software
    Best Practices Using June 27, 2012 BASE SAS Software Best Practices Using BASE SAS® Software Chapter 1: Best Practices 1.1 Introduction 1.2 Techniques for Conserving CPU and Memory 1.3 Techniques for Minimizing I/O Operations 1.4 Techniques for Conserving Disk Space 1.5 Creating and Using Indexes with SAS Data Sets 1.6 Techniques to Minimize Network Traffic (Self-Study) 2 1 Best Practices Using June 27, 2012 BASE SAS Software What Are Best Practices? Best practices can reduce usage of the following five critical system resources to improve performance: CPU I/O Disk Space Memory Data Storage Space Reducing one resource often increases another. 3 Deciding What Is Important for Efficiency Your Programs Your Site Your Data 4 2 Best Practices Using June 27, 2012 BASE SAS Software Understanding Efficiency at Your Site Hardware Operating Environment System Load SAS Environment 5 Knowing How Your Program Will Be Used The importance of efficiency increases with the following: the complexity of the program or the size of the files being processed the number of times that the program will be executed 6 3 Best Practices Using June 27, 2012 BASE SAS Software Knowing Your Data 7 Considering Trade-Offs In this seminar, many tasks are performed using one or more techniques. To decide which technique is most efficient for a given task, benchmark (measure and compare) the resource usage of each technique. You should benchmark with the actual data to determine which technique is the most efficient. The effectiveness of any efficiency technique depends greatly on the data with which you use the technique.
    [Show full text]
  • Middlebury Library Annual Report: 2018-19
    Middlebury Library Annual Report: 2018-19 Introduction Here at the library, we divide our time among four interconnected activities: designing and managing space, building and maintaining collections, delivering services to support the use of our spaces and collections, and developing ourselves to do all of the above. What follows are the highlights from 2018-19, documenting what was an exhilarating and at times challenging year. Spaces A core part of our mission is to provide spaces that inspire excellence in teaching, study, research, and the production of knowledge. Physical Spaces Library Space Master Plan The Library Space Team worked closely with the architectural firm Marble Fairbanks to ​ create a Master Plan for Davis Family and Armstrong Library. This plan, developed through extensive engagement with our community, will provide us with a long-range plan for how to use our library spaces in the coming years in order to best meet our evolving needs. Digital Spaces Library Website Redesign The library web team worked closely with the Office of Communications to convert the ​ library website to a new design. We created a new information architecture, updated or wrote new copy, and overall simplified the site with an eye to making it even easier for our patrons to find the resources and services that they are looking for. Upgraded Library Catalog We upgraded MIDCAT, our library catalog, to a new version. This allowed us to move our ​ database to the cloud, providing better security and backups, as well as simplifying future system upgrades. At the same time, we merged our library database with that of the Middlebury Institute.
    [Show full text]
  • 111-31: a Pragmatic Programmers Introduction to SAS® Data Integration Studio
    SUGI 31 Hands-on Workshops Paper 111-31 A Pragmatic Programmers Introduction to Data Integration Studio: Hands on Workshop Gregory S. Nelson ThotWave Technologies, Cary, North Carolina ABSTRACT .................................................................................................................................................. 2 INTRODUCTION........................................................................................................................................ 2 THE SAS 9 PLATFORM: AN ARCHITECTURAL PERSPECTIVE ........................................................................ 2 THE WORKSHOP.......................................................................................................................................... 4 The Source Data Model.......................................................................................................................... 4 The Target Data Model .......................................................................................................................... 5 The Business Problem ............................................................................................................................ 5 EXERCISES ................................................................................................................................................. 5 TASK #1: UNDERSTANDING THE USER INTERFACE...................................................................................... 7 Task 1 – Step A: Logging in (authentication)........................................................................................
    [Show full text]
  • Policy-Making for Research Data in Repositories: a Guide
    Policy-making for Research Data in Repositories: A Guide http://www.disc-uk.org/docs/guide.pdf Image © JupiterImages Corporation 2007 May 2009 Version 1.2 by Ann Green Stuart Macdonald Robin Rice DISC-UK DataShare project Data Information Specialists Committee -UK Table of Contents i. Introduction 3 ii. Acknowledgements 4 iii. How to use this guide 4 1. Content Coverage 5 a. Scope: subjects and languages 5 b. Kinds of research data 5 c. Status of the research data 6 d. Versions 7 e. Data file formats 8 f. Volume and size limitations 10 2. Metadata 13 a. Access to metadata 13 b. Reuse of metadata 13 c. Metadata types and sources 13 d. Metadata schemas 16 3. Submission of Data (Ingest) 18 a. Eligible depositors 18 b. Moderation by repository 19 c. Data quality requirements 19 d. Confidentiality and disclosure 20 e. Embargo status 21 f. Rights and ownership 22 4. Access and Reuse of Data 24 a. Access to data objects 24 b. Use and reuse of data objects 27 c. Tracking users and use statistics 29 5. Preservation of Data 30 a. Retention period 30 b. Functional preservation 30 c. File preservation 30 d. Fixity and authenticity 31 6. Withdrawal of Data and Succession Plans 33 7. Next Steps 34 8. References 35 INTRODUCTION The Policy-making for Research Data in Repositories: A Guide is intended to be used as a decision-making and planning tool for institutions with digital repositories in existence or in development that are considering adding research data to their digital collections.
    [Show full text]
  • Research Data Management for Librarians
    Dr Research Data Management for librarians Dublin Core field Title Research Data Management for librarians Creator Jones, Sarah Creator Guy, Marieke Creator Pickton, Miggie Subject Research data management; data sharing; research support; libr arians; skills. Description A course booklet to accompany a three-hour training course for librarians on research data management and how to support researchers . Publisher Digital Curation Centre Date 2013 Type Text; image Format Paper Identifier N/A Source Content expands on the DCC slideset: ‘Research Data Management for librarians’ Language En Relation Format IsBasedOn RoaDMAP training booklet Preserving your research data for future use, University of Leeds http://library.leeds.ac.uk/roadmap -project-outputs Coverage N/A Rights CC-BY - unless indicated otherwise Contents 1. Acknowledgements .............................................................................................................................. 2 2. Tutors.................................................................................................................................................... 2 3. Course structure ................................................................................................................................... 2 4. Aims and objectives .............................................................................................................................. 3 5. Getting started ....................................................................................................................................
    [Show full text]
  • A Pragmatic Programmers Introduction to Data Integration Studio: a Hands on Workshop
    Paper HW05 A Pragmatic Programmers Introduction to Data Integration Studio: Hands on Workshop Gregory S. Nelson ThotWave Technologies, Cary, North Carolina ABSTRACT .................................................................................................................................................. 2 INTRODUCTION........................................................................................................................................ 2 THE SAS 9 PLATFORM: AN ARCHITECTURAL PERSPECTIVE ........................................................................ 2 THE WORKSHOP.......................................................................................................................................... 4 The Source Data Model.......................................................................................................................... 4 The Target Data Model .......................................................................................................................... 5 The Business Problem ............................................................................................................................ 5 EXERCISES ................................................................................................................................................. 5 TASK #1: UNDERSTANDING THE USER INTERFACE...................................................................................... 7 Task 1 – Step A: Logging in (authentication)........................................................................................
    [Show full text]
  • Productivity Through Data Management A.K.A
    Productivity through data management a.k.a. Writing an effective data management plan Anna J Dabrowski 1 Introduction Focus on planning for effective data management in advance of research projects. Goal: Gain an overview of information to include in a plan. Slides: http://hdl.handle.net/1969.1/166284 2 Defining “data” “Recorded factual material commonly accepted in the scientific community as necessary to document and support research findings.” (National Institutes of Health) “…determined by the community of interest through the process of peer review and program management.” (National Science Foundation) “…materials generated or collected during the course of conducting research.” (National Endowment for the Humanities) 3 The research data lifecycle University of Virginia Library. Steps in the Data Life Cycle. 4 http://data.library.virginia.edu/data-management/lifecycle/ Time is the enemy - Accumulating large quantities of disorganized files. - Lack of information describing content in digital files. - Changes to hardware, software, and file formats in common use. - File corruption. - Failure of storage media. - Data leaving with collaborators. 5 Research data management (RDM) The practices in place for - organizing, - documenting, - storing, - sharing, - and preserving data collected during a research project. 6 Data management plans (DMPs) Structured documents created in advance of collecting data that: - Help you think about data over time and in context; - Provide a framework for documenting your RDM practices; - Are required by many grant agencies that fund research. 7 Scope of DMPs - A research group or collaboration; - An individual researcher; - A specific research project. 8 Two perspectives Concerned with efficient practices that help to maintain access to usable data.
    [Show full text]
  • Proceedings of the 8Th Annual Federal
    Proceedings of the 8^^ Annual Federal Depository Library Conference April 12 - 15, 1999 Library Programs Service U.S. Government Printing Office Wasfiington, DC 20401 U.S. Government Printing Office Micliael F. DiMario, Public Printer Superintendent of Documents Francis ). Buclcley, ]r. Library Programs Service Gil Baldwin, Director Library Division Sheila M. McGarr, Chief Proceedings of the 8^^ Annual Federal Depository Library Conference April 12- 15, 1999 Holiday Inn-Bethesda Bethesda, MD Library Programs Service U.S. Government Printing Office Washington, DC 20401 1999 Marian W. MacGilvray Editor Any use of trade, product, or firm names in tliis publication is for descriptive purposes only and does not imply endorsement by the U.S. Government. 1999 Federal Depository Library Conference - Proceedings Table of Contents Agenda v Developing Regional Web Pages for Selectives: Suzanne Holcombe, Steve Beleu 1 FEFDL: Florida Electronic Federal Depository Library: Jan Swanbeck 5 Incorporating the NRC Legacy Collection into the FDLP: George Barnum, Elizabeth Yeates 8 Videoconferencing: Sandra Fritz 10 Introduction to DVD: Carol Cini 16 Partners Providing Public Access: Pennsylvania Spatial Data Access: A Partnership to Provide Access to Government Data: Kenwood Giffhorn 19 Partners Providing Public Access: The Library's Role in the PASDA Partnership: Chris Pfeiffer ....2^ Partners Providing Public Access: Focusing the Library's Contribution: Todd Bacastow 22 National Geospatial Data Clearinghouse: Archie Warnock 24 NTIS-GPO Depository Library Imaging Pilot Project: Walter L Finch 27 Assessment of Electronic Government Information Products: Final Report: Forest Woody Norton 29 DOE Virtual Library of Energy Science and Technology: Walter L. Warnick 33 Building the FDLP Electronic Collection: Laurie B.
    [Show full text]
  • Trends at a Glance: a Management Dashboard of Library Statistics | Morton-Owens and Hanson 37
    Trends at a Glance: A Management Emily Morton-Owens Dashboard of Library Statistics and Karen L. Hanson ABSTRACT Systems librarians at an academic medical library created a management data dashboard. Charts were designed using best practices for data visualization and dashboard layout, and include metrics on gatecount, website visits, instant message reference chats, circulation, and interlibrary loan volume and turnaround time. Several charts draw on EZproxy log data that has been analyzed and linked to other databases to reveal use by different academic departments and user roles (such as faculty or student). Most charts are bar charts and include a linear regression trend line. The implementation uses Perl scripts to retrieve data from eight different sources and add it to a MySQL data warehouse, from which PHP/JavaScript webpages use Google Chart Tools to create the dashboard charts. INTRODUCTION New York University Health Sciences Libraries (NYUHSL) had adopted a number of systems that were either open-source, home-grown, or that offered APIs of one sort or another. Examples include Drupal, Google Analytics, and a home-grown interlibrary loan (ILL) system. Systems librarians decided to capitalize on the availability of this data by designing a system that would give library management a single, continuously self-updating point of access to monitor a variety of metrics. Previously this kind of information had been assembled annually for surveys like AAHSL and ARL. 1 The layout and scope of the dashboard was influenced by Google Analytics and a beta dashboard project at Brown.2 The dashboard enables closer scrutiny of trends in library use, ideally resulting in a more agile response to problems and opportunities.
    [Show full text]