Visual exploration of frequent patterns in multivariate time series

Hao, Ming; Marwah, Manish; Janetzko, Halldor; Dayal, Umeshwar; Keim, Daniel A.; Patnaik, Debprakash; Ramakrishnan, Naren; Sharma, Ratnesh

Visual exploration of frequent patterns in multivariate time series

Dateien

Hao_190757.pdfGröße: 4.92 MBDownloads: 552

Datum

2012

Autor:innen

Hao, Ming

Marwah, Manish

Janetzko, Halldor

Dayal, Umeshwar

Keim, Daniel A.

Patnaik, Debprakash

Ramakrishnan, Naren

Sharma, Ratnesh

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Information Visualization. 2012, 11(1), pp. 71-83. ISSN 1473-8716. eISSN 1473-8724. Available under: doi: 10.1177/1473871611430769

Zusammenfassung

The detection of frequently occurring patterns, also called motifs, in data streams has been recognized as an important task. To find these motifs, we use an advanced event encoding and pattern discovery algorithm. As a large time series can contain hundreds of motifs, there is a need to support interactive analysis and exploration. In addition, for certain applications, such as data center resource management, service managers want to be able to predict the next day’s power consumption from the previous months’ data. For this purpose, we introduce four novel visual analytics methods: (i) motif layout – using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs; (ii) motif distortion – enlarging or shrinking motifs for visualizing them more clearly; (iii) motif merging – combining a number of identical adjacent motif instances to simplify the display; and (iv) pattern preserving prediction – using a pattern-preserving smoothing and prediction algorithm to provide a reliable prediction for seasonal data. We have applied these methods to three real-world datasets: data center chilling utilization, oil well production, and system resource utilization. The results enable service managers to interactively examine motifs and gain new insights into the recurring patterns to analyze system operations. Using the above methods, we have also predicted both power consumption and server utilization in data centers with an accuracy of 70–80%.

Fachgebiet (DDC)

004 Informatik

Zitieren

ISO 690

HAO, Ming, Manish MARWAH, Halldor JANETZKO, Umeshwar DAYAL, Daniel A. KEIM, Debprakash PATNAIK, Naren RAMAKRISHNAN, Ratnesh SHARMA, 2012. Visual exploration of frequent patterns in multivariate time series. In: Information Visualization. 2012, 11(1), pp. 71-83. ISSN 1473-8716. eISSN 1473-8724. Available under: doi: 10.1177/1473871611430769

BibTex

@article{Hao2012Visua-19075,
  year={2012},
  doi={10.1177/1473871611430769},
  title={Visual exploration of frequent patterns in multivariate time series},
  number={1},
  volume={11},
  issn={1473-8716},
  journal={Information Visualization},
  pages={71--83},
  author={Hao, Ming and Marwah, Manish and Janetzko, Halldor and Dayal, Umeshwar and Keim, Daniel A. and Patnaik, Debprakash and Ramakrishnan, Naren and Sharma, Ratnesh}
}

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Universitätsbibliographie

Ja