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Big Data Factories. Collaborative Approaches

Author

Listed:
  • Sorin Matei

    (CERIAS - Center for Education and Research in Information Assurance and Security (Purdue University) - Purdue University [West Lafayette])

  • Nicolas Jullien

    (LUSSI - Département Logique des Usages, Sciences sociales et Sciences de l'Information - UEB - Université européenne de Bretagne - European University of Brittany - Télécom Bretagne - IMT - Institut Mines-Télécom [Paris], MARSOUIN - Môle Armoricain de Recherche sur la SOciété de l'information et des usages d'INternet - UR - Université de Rennes - UEB - Université européenne de Bretagne - European University of Brittany - UBS - Université de Bretagne Sud - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - UBO - Université de Brest - Télécom Bretagne - IMT - Institut Mines-Télécom [Paris] - UR2 - Université de Rennes 2, LEGO - Laboratoire d'Economie et de Gestion de l'Ouest - UBS - Université de Bretagne Sud - UBO - Université de Brest - IMT - Institut Mines-Télécom [Paris] - IBSHS - Institut Brestois des Sciences de l'Homme et de la Société - UBO - Université de Brest - UBL - Université Bretagne Loire - IMT Atlantique - IMT Atlantique - IMT - Institut Mines-Télécom [Paris])

  • Sean Patrick Goggins

    (Mizzou - University of Missouri [Columbia] - University of Missouri System)

Abstract

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

Suggested Citation

  • Sorin Matei & Nicolas Jullien & Sean Patrick Goggins, 2017. "Big Data Factories. Collaborative Approaches," Post-Print hal-01614056, HAL.
    • Handle: RePEc:hal:journl:hal-01614056
    as

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