Big Data Factories -

Big Data Factories

Collaborative Approaches
Buch | Hardcover
VI, 141 Seiten
2017 | 1st ed. 2017
Springer International Publishing (Verlag)
978-3-319-59185-8 (ISBN)
37,44 inkl. MwSt
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

Sorin Matei is a Professor at Brian Lamb School of Communication at Purdue University. His focus areas are computational social science, collaborative content production, and data storytelling. Nicolas Jullien is an Associate Professor at the LUSSI Department of Telecom Bretagne. 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.

Chapter1. Introduction.- Part 1: Theoretical Principles and Approaches to Data Factories.- Chapter2. Accessibility and Flexibility: Two Organizing Principles for Big Data Collaboration.- Chapter3. The Open Community Data Exchange: Advancing Data Sharing and Discovery in Open Online Community Science.- Part 2: Theoretical principles and ideas for designing and deploying data factory approaches.- Chapter4. Levels of Trace Data for Social and Behavioral Science Research.- Chapter5. The 10 Adoption Drivers of Open Source Software that Enables e-Research in Data Factories for Open Innovations.- Chapter6. Aligning online social collaboration data around social order: theoretical considerations and measures.- Part 3: Approaches in action through case studies of data based research, best practice scenarios, or educational briefs.- Chapter7. Lessons learned from a decade of FLOSS data collection.- Chapter8. Teaching Students How (NOT) to Lie, Manipulate, and Mislead with Information Visualizations.- Chapter9. Democratizing Data Science: The Community Data Science Workshops and Classes.

Erscheinungsdatum
Reihe/Serie Computational Social Sciences
Zusatzinfo VI, 141 p. 18 illus., 14 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 388 g
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Schlagworte alphabet of social interaction • Big Data/Analytics • Bioinformatics • business mathematics & systems • Business mathematics & systems • Computer applications in the social & behavioural • Computer applications in the social & behavioural • Computer Appl. in Social and Behavioral Sciences • Computer Science • creating collaborative spaces • Data Mining • data mining and knowledge discovery • data recombination and reuse • Ethics & Moral Philosophy • Ethics & moral philosophy • Expert systems / knowledge-based systems • factoring data • fungible big data sets • Information technology: general issues • large scale data privacy and security • Molecular Biology • networks of online interaction • philosophy of science • research ethics • trends in data collection
ISBN-10 3-319-59185-1 / 3319591851
ISBN-13 978-3-319-59185-8 / 9783319591858
Zustand Neuware
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