Publikation:

Visualizing frequent patterns in large multivariate time series

Lade...
Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2011

Autor:innen

Hao, Ming
Marwah, Manish
Sharma, Ratnesh
Dayal, Umeshwar
Patnaik, Debprakash
Ramakrishnan, Naren

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

DOI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

WONG, Pak Chung, ed. and others. Visualization and Data Analysis 2011. SPIE, 2011, pp. 78680J-78680J-10. SPIE Proceedings. 7868. Available under: doi: 10.1117/12.872169

Zusammenfassung

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.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

IS&T/SPIE Electronic Imaging, San Francisco, California
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690HAO, Ming, Manish MARWAH, Halldor JANETZKO, Ratnesh SHARMA, Daniel A. 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, 2011, pp. 78680J-78680J-10. SPIE Proceedings. 7868. Available under: doi: 10.1117/12.872169
BibTex
@inproceedings{Hao2011-01-24Visua-19392,
  year={2011},
  doi={10.1117/12.872169},
  title={Visualizing frequent patterns in large multivariate time series},
  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, Halldor and Sharma, Ratnesh and Keim, Daniel A. and Dayal, Umeshwar and Patnaik, Debprakash and Ramakrishnan, Naren}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/19392">
    <dcterms:issued>2011-01-24</dcterms:issued>
    <dc:contributor>Patnaik, Debprakash</dc:contributor>
    <dc:contributor>Janetzko, Halldor</dc:contributor>
    <dc:contributor>Ramakrishnan, Naren</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dcterms:title>Visualizing frequent patterns in large multivariate time series</dcterms:title>
    <dc:contributor>Marwah, Manish</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-06-28T09:45:49Z</dcterms:available>
    <dc:creator>Patnaik, Debprakash</dc:creator>
    <dc:creator>Janetzko, Halldor</dc:creator>
    <dc:creator>Hao, Ming</dc:creator>
    <dc:language>eng</dc:language>
    <dc:contributor>Hao, Ming</dc:contributor>
    <dc:creator>Marwah, Manish</dc:creator>
    <dcterms:bibliographicCitation>Publ. in: Visualization and data analysis 2011 : 24 - 25 January 2011, California, United States ; [part of] IS&amp;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</dcterms:bibliographicCitation>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/19392"/>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:contributor>Dayal, Umeshwar</dc:contributor>
    <dc:creator>Ramakrishnan, Naren</dc:creator>
    <dc:creator>Dayal, Umeshwar</dc:creator>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:contributor>Sharma, Ratnesh</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-06-28T09:45:49Z</dc:date>
    <dc:creator>Sharma, Ratnesh</dc:creator>
    <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. 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.</dcterms:abstract>
    <dc:rights>terms-of-use</dc:rights>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen