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Visual Analytics for Understanding Temporal Distributions and Variations

In: Visual Analytics for Data Scientists

Author

Listed:
  • Natalia Andrienko

    (Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
    City, University of London, Northampton Square, Department of Computer Science)

  • Gennady Andrienko

    (Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
    City, University of London, Northampton Square, Department of Computer Science)

  • Georg Fuchs

    (Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven)

  • Aidan Slingsby

    (City, University of London, Northampton Square, Department of Computer Science)

  • Cagatay Turkay

    (University of Warwick, Centre for Interdisciplinary Methodologies)

  • Stefan Wrobel

    (Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
    University of Bonn)

Abstract

There are two major types of temporal data, events and time series of attribute values, and there are methods for transforming one of them into the other. For events, a general analysis task is to understand how they are distributed in time. For time series, as well as for events of diverse kinds, a general task is to understand how the attribute values or the kinds of occurring events vary over time. In analysing temporal distributions and variations, it is essential to account for the specific features of time and temporal phenomena, particularly, recurring cycles and temporal dependence. To see patterns of temporal distribution or variation, people commonly apply visual displays where one dimension represents time and the other is used to show individual events, attribute values, or statistical summaries. To see the data in the context of temporal cycles, a common approach is to use a 2D display where one or two cycles are represented by display dimensions. For large and/or complex data, visual displays need to be combined with techniques for computational analysis, such as clustering, embedding, sequence mining, and motif discovery. We show and discuss examples of employing such combinations in application to different data and analysis tasks.

Suggested Citation

  • Natalia Andrienko & Gennady Andrienko & Georg Fuchs & Aidan Slingsby & Cagatay Turkay & Stefan Wrobel, 2020. "Visual Analytics for Understanding Temporal Distributions and Variations," Springer Books, in: Visual Analytics for Data Scientists, chapter 0, pages 229-260, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-56146-8_8
    DOI: 10.1007/978-3-030-56146-8_8
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