Hidden Bibliographic Details
| Other authors / contributors: | Matei, Sorin Adam.
Jullien, Nicolas.
Goggins, Sean P.
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| ISBN: | 9783319591865
331959186X
9783319591858
3319591851
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| Digital file characteristics: | text file PDF
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| Notes: | Includes bibliographical references and index.
Print version record.
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| Summary: | 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.
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| Other form: | Print version: Matei, Sorin Adam. Big Data Factories : Collaborative Approaches. Cham : Springer International Publishing AG, z. Hd. Alexander Grossmann, ©2017 9783319591858
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| Standard no.: | 10.1007/978-3-319-59186-5
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