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Modelling Processes of Big Data Analytics

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Web Information Systems Engineering – WISE 2015 (WISE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9418))

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Abstract

Analytics tasks in scientific and industrial environments are to be performed in some order that, as a whole, represent the rationale of a specific process on the data. The challenge to process the data is, beyond there mere size, their dispersion and the variety of their formats. The data analysis may include a range of tasks to be executed on a range of query engines, which are created by various users, such as business analysts, engineers, end-users etc. The users, depending on their role and expertise, may need or care for a different level of abstraction with respect to the execution of the individual tasks and overall process. Therefore, a system for Big Data analytics should enable the expression of tasks in an abstract manner, adaptable to the user role, interest and expertise. In this work we discuss the modelling of Big Data Analytics. We propose a novel representation model for analytics tasks and overall processes, that encapsulates their declaration, but, also, their execution semantics. The model allows for the definition of analytics processes with a varying level of abstraction, adaptable to the user role. Our motivation derives from real use cases.

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Notes

  1. 1.

    http://www.asap-fp7.eu.

  2. 2.

    Replication of tasks using many associative vertices that correspond to the same task of an original vertex may be needed for optimisation of workflow execution.

  3. 3.

    Note that such tasks may involve also human interaction and may be performed online or offline.

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Acknowledgment

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement \(n^o\) 619706 ASAP.

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Authors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Verena Kantere & Maxim Filatov

Authors
  1. Verena Kantere
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  2. Maxim Filatov
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Corresponding author

Correspondence to Verena Kantere .

Editor information

Editors and Affiliations

  1. Tsinghua University, Bijing, China

    Jianyong Wang

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. Florida Atlantic University, Boca Raton, Florida, USA

    Dingding Wang

  4. Victoria University, Melbourne, Australia

    Hua Wang

  5. School of Computing & Information, Florida International University, Miami, Florida, USA

    Shu-Ching Chen

  6. Florida International University, Miami, Florida, USA

    Tao Li

  7. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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© 2015 Springer International Publishing Switzerland

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Kantere, V., Filatov, M. (2015). Modelling Processes of Big Data Analytics. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_21

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  • DOI: https://doi.org/10.1007/978-3-319-26190-4_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26189-8

  • Online ISBN: 978-3-319-26190-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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