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The Ten Adoption Drivers of Open Source Software That Enables e-Research in Data Factories for Open Innovations

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Big Data Factories

Part of the book series: Computational Social Sciences ((CSS))

Abstract

This chapter describes ten drivers of the adoption of open source software that enables e-research in data factories for open innovations. More specifically, the chapter discusses the emerging phenomena of big data and e-research, along with their various defining characteristics. Then the chapter makes a case for the importance of understanding the adoption of open source software for processing and harnessing big data. In other words, big data which remain in the raw form will continue to be big data with hidden insights uncovered without the adoption of appropriate software. Open source software applications, along with the larger concept of cyberinfrastructure, play a critical role in our ability to optimize the full potential of big data. The chapter also includes critical questions community stakeholders should keep in mind when promoting the diffusion and dissemination of good software applications that will support data factories for open innovations.

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Acknowledgment

The author thanks Mona Sleiman, Rion Dooley, Nancy Wilkins-Diehr, and John Towns for their support of this project. This research was funded by NSF ACI 1322305.

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Authors and Affiliations

  1. School of Communication, Chapman University, Orange, CA, 92866, USA

    Kerk F. Kee

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  1. Kerk F. Kee
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Correspondence to Kerk F. Kee .

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Editors and Affiliations

  1. Purdue University, West Lafayette, Indiana, USA

    Sorin Adam Matei

  2. Technopôle Brest-Iroise, IMT Atlantique (Telecom Bretagne), Brest Cedex 3, France

    Nicolas Jullien

  3. Computer Science, University of Missouri, Columbia, Missouri, USA

    Sean P. Goggins

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Kee, K.F. (2017). The Ten Adoption Drivers of Open Source Software That Enables e-Research in Data Factories for Open Innovations. In: Matei, S., Jullien, N., Goggins, S. (eds) Big Data Factories. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-59186-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-59186-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59185-8

  • Online ISBN: 978-3-319-59186-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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