Visualizing frequent patterns in large multivariate time series

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HAO, Ming, Manish MARWAH, Halldór JANETZKO, Ratnesh SHARMA, Daniel KEIM, Umeshwar DAYAL, Debprakash PATNAIK, Naren RAMAKRISHNAN, 2011. Visualizing frequent patterns in large multivariate time series. IS&T/SPIE Electronic Imaging. San Francisco, California. In: WONG, Pak Chung, ed. and others. Visualization and Data Analysis 2011. SPIE, pp. 78680J-78680J-10. Available under: doi: 10.1117/12.872169

@inproceedings{Hao2011-01-24Visua-19392, title={Visualizing frequent patterns in large multivariate time series}, year={2011}, doi={10.1117/12.872169}, number={7868}, publisher={SPIE}, series={SPIE Proceedings}, booktitle={Visualization and Data Analysis 2011}, pages={78680J--78680J-10}, editor={Wong, Pak Chung}, author={Hao, Ming and Marwah, Manish and Janetzko, Halldór and Sharma, Ratnesh and Keim, Daniel and Dayal, Umeshwar and Patnaik, Debprakash and Ramakrishnan, Naren} }

Janetzko, Halldór Hao, Ming 2012-06-28T09:45:49Z 2011-01-24 Patnaik, Debprakash Marwah, Manish 2012-06-28T09:45:49Z Keim, Daniel Janetzko, Halldór Dayal, Umeshwar Sharma, Ratnesh Dayal, Umeshwar 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. Publ. in: Visualization and data analysis 2011 : 24 - 25 January 2011, California, United States ; [part of] IS&T/SPIE electronic imaging, science and technology / Pak Chung Wong ... (Eds). - Bellingham, Wash. : SPIE, 2011. - 78680J [17]. - (Proceedings of SPIE ; 7868). - ISBN 978-0-8194-8405-5 eng Sharma, Ratnesh Keim, Daniel Hao, Ming Ramakrishnan, Naren terms-of-use Marwah, Manish Ramakrishnan, Naren Visualizing frequent patterns in large multivariate time series Patnaik, Debprakash

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