Table of contents
About this book
The book proposes a systematic approach to big data collection, documentation and development of analytic procedures that foster collaboration on a large scale. This approach, designated as “data factoring” emphasizes the need to think of each individual dataset developed by an individual project as part of a broader data ecosystem, easily accessible and exploitable by parties not directly involved with data collection and documentation. Furthermore, data factoring uses and encourages pre-analytic operations that add value to big data sets, especially recombining and repurposing.
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
trends in data collection data recombination and reuse creating collaborative spaces fungible big data sets factoring data alphabet of social interaction networks of online interaction large scale data privacy and security
Editors and affiliations
- Book Title Big Data Factories
- Book Subtitle Collaborative Approaches
Sorin Adam Matei
Sean P. Goggins
- Series Title Computational Social Sciences
- Series Abbreviated Title Comp. Soc. Sci.
- DOI https://doi.org/10.1007/978-3-319-59186-5
- Copyright Information Springer International Publishing AG 2017
- Publisher Name Springer, Cham
- eBook Packages Computer Science Computer Science (R0)
- Hardcover ISBN 978-3-319-59185-8
- Softcover ISBN 978-3-319-86564-5
- eBook ISBN 978-3-319-59186-5
- 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
Data Mining and Knowledge Discovery
Computer Appl. in Social and Behavioral Sciences
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