Visual analysis of frequent patterns in large time series


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HAO, Ming C., Manish MARWAH, Halldór JANETZKO, Daniel KEIM, Umeshwar DAYAL, Rohit SHARMA, Devdutt PATNAIK, Naren RAMAKRISHNAN, 2010. Visual analysis of frequent patterns in large time series. 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST). Salt Lake City, UT, USA, 25. Okt 2010 - 26. Okt 2010. In: 2010 IEEE Symposium on Visual Analytics Science and Technology. Piscataway, NJ:IEEE, pp. 227-228. ISBN 978-1-4244-9488-0. Available under: doi: 10.1109/VAST.2010.5650766

@inproceedings{Hao2010-10Visua-40488, title={Visual analysis of frequent patterns in large time series}, year={2010}, doi={10.1109/VAST.2010.5650766}, isbn={978-1-4244-9488-0}, address={Piscataway, NJ}, publisher={IEEE}, booktitle={2010 IEEE Symposium on Visual Analytics Science and Technology}, pages={227--228}, author={Hao, Ming C. and Marwah, Manish and Janetzko, Halldór and Keim, Daniel and Dayal, Umeshwar and Sharma, Rohit and Patnaik, Devdutt and Ramakrishnan, Naren} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dc:contributor>Hao, Ming C.</dc:contributor> <dc:creator>Dayal, Umeshwar</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:date rdf:datatype="">2017-11-06T14:40:43Z</dc:date> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:isPartOf rdf:resource=""/> <dcterms:title>Visual analysis of frequent patterns in large time series</dcterms:title> <dc:contributor>Sharma, Rohit</dc:contributor> <dc:creator>Marwah, Manish</dc:creator> <dc:creator>Janetzko, Halldór</dc:creator> <dc:creator>Patnaik, Devdutt</dc:creator> <dc:contributor>Dayal, Umeshwar</dc:contributor> <dc:contributor>Ramakrishnan, Naren</dc:contributor> <dc:creator>Ramakrishnan, Naren</dc:creator> <dc:creator>Sharma, Rohit</dc:creator> <dc:creator>Keim, Daniel</dc:creator> <dc:contributor>Marwah, Manish</dc:contributor> <dc:creator>Hao, Ming C.</dc:creator> <dc:language>eng</dc:language> <dspace:isPartOfCollection rdf:resource=""/> <dcterms:issued>2010-10</dcterms:issued> <dc:contributor>Janetzko, Halldór</dc:contributor> <bibo:uri rdf:resource=""/> <dcterms:available rdf:datatype="">2017-11-06T14:40:43Z</dcterms:available> <dc:contributor>Patnaik, Devdutt</dc:contributor> <dc:contributor>Keim, Daniel</dc:contributor> <dcterms:abstract xml:lang="eng">The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. To find these motifs, we use an advanced temporal data mining algorithm. Since our algorithm usually finds hundreds of motifs, we need to analyze and access the discovered motifs. For this purpose, 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. We have applied and evaluated our methods using two real-world data sets: data center cooling and oil well production.</dcterms:abstract> </rdf:Description> </rdf:RDF>

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