Big Data Factories
Springer International Publishing (Verlag)
978-3-319-59185-8 (ISBN)
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 | 25.12.2017 |
|---|---|
| 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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