Visual pattern discovery in timed event data

Matthias Schaefer; Franz Wanner; Florian Mansmann; Christian Scheible; Verity Stennett; Anders T. Hasselrot; Daniel A. Keim

doi:10.1117/12.871870

24 January 2011 Visual pattern discovery in timed event data

Matthias Schaefer, Franz Wanner, Florian Mansmann, Christian Scheible, Verity Stennett, Anders T. Hasselrot, Daniel A. Keim

Author Affiliations +

Proceedings Volume 7868, Visualization and Data Analysis 2011; 78680K (2011) https://doi.org/10.1117/12.871870
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

Publisher’s Note: This paper, originally published on 24 January 2011, was replaced with a corrected/revised version on 9 April 2015. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. Business processes have tremendously changed the way large companies conduct their business: The integration of information systems into the workflows of their employees ensures a high service level and thus high customer satisfaction. One core aspect of business process engineering are events that steer the workflows and trigger internal processes. Strict requirements on interval-scaled temporal patterns, which are common in time series, are thereby released through the ordinal character of such events. It is this additional degree of freedom that opens unexplored possibilities for visualizing event data. In this paper, we present a flexible and novel system to find significant events, event clusters and event patterns. Each event is represented as a small rectangle, which is colored according to categorical, ordinal or intervalscaled metadata. Depending on the analysis task, different layout functions are used to highlight either the ordinal character of the data or temporal correlations. The system has built-in features for ordering customers or event groups according to the similarity of their event sequences, temporal gap alignment and stacking of co-occurring events. Two characteristically different case studies dealing with business process events and news articles demonstrate the capabilities of our system to explore event data.

Citation Download Citation

Matthias Schaefer, Franz Wanner, Florian Mansmann, Christian Scheible, Verity Stennett, Anders T. Hasselrot, and Daniel A. Keim "Visual pattern discovery in timed event data", Proc. SPIE 7868, Visualization and Data Analysis 2011, 78680K (24 January 2011); https://doi.org/10.1117/12.871870

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