Importance-Driven Visualization Layouts for Large Time Series Data

Lade...
Vorschaubild
Dateien
infovis05.pdf
infovis05.pdfGröße: 1.25 MBDownloads: 769
Datum
2005
Autor:innen
Hao, Ming C.
Dayal, Umeshwar
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
Open Access Green
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
STASKO, John T., ed. and others. IEEE Symposium on Information Visualization (InfoVis 2005), Minneapolis, MN, USA, October 23-25, 2005. 2005. ISBN 0-7803-9464-X
Zusammenfassung

Time series are an important type of data with applications in virtually every aspect of the real world. Often a large number of time series have to be monitored and analyzed in parallel. Sets of time series may show intrinsic hierarchical relationships and varying degrees of importance among the individual time series. Effective techniques for visually analyzing large sets of time series should encode the relative importance and hierarchical ordering of the time series data by size and position, and should also provide a high degree of regularity in order to support comparability by the analyst. In this paper, we present a framework for visualizing large sets of time series. Based on the notion of inter time series importance relationships, we define a set of objective functions that space-filling layout schemes for time series data should obey. We develop an efficient algorithm addressing the identified problems by generating layouts that reflect hierarchyand importance-based relationships in a regular layout with favorable aspect ratios. We apply our technique to a number of real-world data sets including sales and stock data, and we compare our technique with an aspect ratio aware variant of the well-known TreeMap algorithm. The examples show the advantages and practical usefulness of our layout algorithm.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Information Visualization, Time Series, Space-Filling Layout Generation
Konferenz
Rezension
undefined / . - undefined, undefined
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Datensätze
Zitieren
ISO 690HAO, Ming C., Umeshwar DAYAL, Daniel A. KEIM, Tobias SCHRECK, 2005. Importance-Driven Visualization Layouts for Large Time Series Data. In: STASKO, John T., ed. and others. IEEE Symposium on Information Visualization (InfoVis 2005), Minneapolis, MN, USA, October 23-25, 2005. 2005. ISBN 0-7803-9464-X
BibTex
@inproceedings{Hao2005Impor-5559,
  year={2005},
  title={Importance-Driven Visualization Layouts for Large Time Series Data},
  isbn={0-7803-9464-X},
  booktitle={IEEE Symposium on Information Visualization (InfoVis 2005), Minneapolis, MN, USA, October 23-25, 2005},
  editor={Stasko, John T.},
  author={Hao, Ming C. and Dayal, Umeshwar and Keim, Daniel A. and Schreck, Tobias}
}
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/123456789/5559">
    <dc:contributor>Hao, Ming C.</dc:contributor>
    <dc:creator>Dayal, Umeshwar</dc:creator>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5559"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:abstract xml:lang="eng">Time series are an important type of data with applications in virtually every aspect of the real world. Often a large number of time series have to be monitored and analyzed in parallel. Sets of time series may show intrinsic hierarchical relationships and varying degrees of importance among the individual time series. Effective techniques for visually analyzing large sets of time series should encode the relative importance and hierarchical ordering of the time series data by size and position, and should also provide a high degree of regularity in order to support comparability by the analyst. In this paper, we present a framework for visualizing large sets of time series. Based on the notion of inter time series importance relationships, we define a set of objective functions that space-filling layout schemes for time series data should obey. We develop an efficient algorithm addressing the identified problems by generating layouts that reflect hierarchyand importance-based relationships in a regular layout with favorable aspect ratios. We apply our technique to a number of real-world data sets including sales and stock data, and we compare our technique with an aspect ratio aware variant of the well-known TreeMap algorithm. The examples show the advantages and practical usefulness of our layout algorithm.</dcterms:abstract>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:56:24Z</dcterms:available>
    <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5559/1/infovis05.pdf"/>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:issued>2005</dcterms:issued>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:format>application/pdf</dc:format>
    <dcterms:bibliographicCitation>First publ. in: IEEE Symposium on Information Visualization (InfoVis 2005), Minneapolis, MN, USA, October 23-25, 2005</dcterms:bibliographicCitation>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/2.0/"/>
    <dc:creator>Hao, Ming C.</dc:creator>
    <dc:contributor>Dayal, Umeshwar</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:56:24Z</dc:date>
    <dcterms:title>Importance-Driven Visualization Layouts for Large Time Series Data</dcterms:title>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:language>eng</dc:language>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5559/1/infovis05.pdf"/>
    <dc:creator>Schreck, Tobias</dc:creator>
  </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