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Modeling and Processing of Time Interval Data for Data-Driven Decision Support

In: Automation, Communication and Cybernetics in Science and Engineering 2015/2016

Author

Listed:
  • Philipp Meisen

    (IMA/ZLW & IfU, RWTH Aachen University)

  • Tobias Meisen

    (IMA/ZLW & IfU, RWTH Aachen University)

  • Marco Recchioni

    (IMA/ZLW & IfU, RWTH Aachen University)

  • Daniel Schilberg

    (IMA/ZLW & IfU, RWTH Aachen University)

  • Sabina Jeschke

    (IMA/ZLW & IfU, RWTH Aachen University)

Abstract

Over the past decades, several disciplines like artificial intelligence, music, medicine, ergonomics or cognitive science dealt with problems concerning analyses of data associated with time intervals. Topics like pattern recognition, comparison, quality, or visualization are in focus of current research. Using these techniques in the context of data-driven decision support is quite rare even though the importance of data to support better decision making can be enormous. Reasons lie above all in limited insufficient tooling support, expensive data processing, and inapplicable requirements. In this paper, we discuss the use of time interval data and name difficulties arising when processing such data for data-driven decision support. We discuss and present solutions for overcoming the identified problems and enabling the usage of time interval data for data-driven decision support. We introduce a time interval data analysis model that provides fast access to the raw time interval data but especially to aggregated time series, mostly needed when making meaningful decisions.

Suggested Citation

  • Philipp Meisen & Tobias Meisen & Marco Recchioni & Daniel Schilberg & Sabina Jeschke, 2016. "Modeling and Processing of Time Interval Data for Data-Driven Decision Support," Springer Books, in: Sabina Jeschke & Ingrid Isenhardt & Frank Hees & Klaus Henning (ed.), Automation, Communication and Cybernetics in Science and Engineering 2015/2016, pages 923-940, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-42620-4_69
    DOI: 10.1007/978-3-319-42620-4_69
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