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Proceedings Paper

Visualizing frequent patterns in large multivariate time series
Author(s): M. Hao; M. Marwah; H. Janetzko; R. Sharma; D. A. Keim; U. Dayal; D. Patnaik; N. Ramakrishnan
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Paper Abstract

The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. However, it is difficult to discover and visualize these motifs as their numbers increase, especially in large multivariate time series. To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. Then we quantify the efficiency of the discovered motifs by linking them with a performance metric. To visualize frequent patterns in a large time series with potentially hundreds of nested motifs on a single display, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. Analysts can interactively optimize the degree of distortion and merging to get the best possible view. A specific motif (e.g., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. We have applied these methods to two real-world data sets: data center cooling and oil well production. The results provide important new insights into the recurring patterns.

Paper Details

Date Published: 24 January 2011
PDF: 10 pages
Proc. SPIE 7868, Visualization and Data Analysis 2011, 78680J (24 January 2011); doi: 10.1117/12.872169
Show Author Affiliations
M. Hao, Hewlett-Packard Labs. (United States)
M. Marwah, Hewlett-Packard Labs. (United States)
H. Janetzko, Univ. Konstanz (Germany)
R. Sharma, Hewlett-Packard Labs. (United States)
D. A. Keim, Univ. Konstanz (Germany)
U. Dayal, Hewlett-Packard Labs. (United States)
D. Patnaik, Virginia Polytechnic Institute and State Univ. (United States)
N. Ramakrishnan, Virginia Polytechnic Institute and State Univ. (United States)


Published in SPIE Proceedings Vol. 7868:
Visualization and Data Analysis 2011
Pak Chung Wong; Jinah Park; Ming C. Hao; Chaomei Chen; Katy Börner; David L. Kao; Jonathan C. Roberts, Editor(s)

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