Overview
- Provides basic researchers and practitioners direct guidelines and best case scenarios for developing activities related to data factoring
- Presents methods for teaching data factoring
- Proposes a set of principles for developing data factoring
Part of the book series: Computational Social Sciences (CSS)
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Table of contents (9 chapters)
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Theoretical Principles and Approaches to Data Factories
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Theoretical Principles and Ideas for Designing and Deploying Data Factory Approaches
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Approaches in Action Through Case Studies of Data Based Research, Best Practice Scenarios, or Educational Briefs
Keywords
About this book
The book proposes a research-development agenda that can undergird an ideal data factory approach. Several programmatic chapters discuss specialized issues involved in data factoring (documentation, meta-data specification, building flexible, yet comprehensive data ontologies, usability issues involved in collaborative tools, etc.). The book also presents case studies for data factoring and processing that can lead to building better scientific collaboration and data sharing strategies and tools.
Finally, the book presents the teaching utility of data factoring and the ethical and privacy concerns related to it.
Chapter 9 of this book is available open access under a CC BY 4.0 license at link.springer.com
Editors and Affiliations
About the editors
Nicolas Jullien is an Associate Professor at the LUSSI Department of Telecom Bretagne. Â His research interests are in open and online communities.
Sean Patrick Goggins is an Associate Professor at Missouri's iSchool, with courtesy appointments as core faculty in the University of Missouri's Informatics Institute and Department of Computer Science.
Bibliographic Information
Book Title: Big Data Factories
Book Subtitle: Collaborative Approaches
Editors: Sorin Adam Matei, Nicolas Jullien, Sean P. Goggins
Series Title: Computational Social Sciences
DOI: https://doi.org/10.1007/978-3-319-59186-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-59185-8Published: 07 December 2017
Softcover ISBN: 978-3-319-86564-5Published: 30 August 2018
eBook ISBN: 978-3-319-59186-5Published: 27 November 2017
Series ISSN: 2509-9574
Series E-ISSN: 2509-9582
Edition Number: 1
Number of Pages: VI, 141
Number of Illustrations: 4 b/w illustrations, 14 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Big Data/Analytics, Bioinformatics, Computer Appl. in Social and Behavioral Sciences, Research Ethics