Visual Analytics for Understanding Temporal Distributions and Variations

Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay and Stefan Wrobel
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Natalia Andrienko: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Gennady Andrienko: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
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

Chapter Chapter 8 in Visual Analytics for Data Scientists, 2020, pp 229-260 from Springer

Abstract: 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.

Date: 2020
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DOI: 10.1007/978-3-030-56146-8_8

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