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Australasian Database Conference

ADC 2016: Databases Theory and Applications pp 245–256Cite as

Prescriptive Analytics for Big Data

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9877))

Abstract

Prescriptive analytics is considered as the next frontier in the area of business analytics. It provides organizations with adaptive, automated, and time-dependent courses of actions to take advantage of likely business opportunities. Given enterprises’ objectives, prescriptive analytics assists them maximize their business values and at the same time mitigates their likely risks by recommending optimal sequences of actions. In this work, a federated prescriptive analytics framework comprising descriptive, predictive and prescriptive components is proposed. The framework also links the extracted insight from the data to their pertinent generated actions. Finally, a few indicative use cases are presented to indicate the necessity of this new analytics paradigm.

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Author information

Authors and Affiliations

  1. RMIT University, Melbourne, Australia

    Reza Soltanpoor

  2. Swinburne University of Technology, Melbourne, Australia

    Timos Sellis

Authors
  1. Reza Soltanpoor
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  2. Timos Sellis
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Corresponding author

Correspondence to Reza Soltanpoor .

Editor information

Editors and Affiliations

  1. Monash University , Clayton, Australia

    Muhammad Aamir Cheema

  2. School of Comp. Science a. Engineer, University of New South Wales School of Comp. Science a. Engineer, Sydney, Australia

    Wenjie Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

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© 2016 Springer International Publishing AG

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Soltanpoor, R., Sellis, T. (2016). Prescriptive Analytics for Big Data. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-46922-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46921-8

  • Online ISBN: 978-3-319-46922-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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