Big Data Factories: Collaborative Approaches (Computational Social Sciences) - Tapa blanda

Libro 12 de 26: Computational Social Sciences
 
9783319865645: Big Data Factories: Collaborative Approaches (Computational Social Sciences)
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ISBN 10: 3319865641 ISBN 13: 9783319865645
Editorial: Springer, 2018

Sinopsis

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

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

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.


De la contraportada

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

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2018
Idioma
Inglés
ISBN 10 
3319865641
ISBN 13 
9783319865645
Encuadernación
Tapa blanda
Número de páginas
148
Editor
Matei Sorin Adam, Jullien Nicolas, Goggins Sean P.
Contacto del fabricante
Springer Nature Customer Service Center GmbH
ProductSafety@springernature.com

Europaplatz 3,69115 Heidelberg, Germany
Heidelberg
69115
Alemania

Otras ediciones populares con el mismo título

9783319591858: Big Data Factories: Collaborative Approaches (Computational Social Sciences)

Edición Destacada

ISBN 10:  3319591851 ISBN 13:  9783319591858
Editorial: Springer, 2017
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