SMARTexplore : Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach

Lade...
Vorschaubild
Dateien
Blumenschein_2-mv29yhuqzckr3.pdf
Blumenschein_2-mv29yhuqzckr3.pdfGröße: 2.17 MBDownloads: 878
Datum
2019
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
SFB TRR 161 TP C 01 Quantitative Messung von Interaktion
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
IEEE Conference on Visual Analytics Science and Technology (VAST) 2018. Piscataway, NJ: IEEE, 2019. ISBN 978-1-5386-6861-0. Available under: doi: 10.1109/VAST.2018.8802486
Zusammenfassung

We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst’s trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing highdimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
High-dimensional data, visual exploration, patterndriven analysis, tabular visualization, subspace, aggregation
Konferenz
IEEE Conference on Visual Analytics Science and Technology (VAST) 2018, 21. Okt. 2018 - 26. Okt. 2018, Berlin, Germany
Rezension
undefined / . - undefined, undefined
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Datensätze
Zitieren
ISO 690BLUMENSCHEIN, Michael, Michael BEHRISCH, Stefanie SCHMID, Simon BUTSCHER, Deborah R. WAHL, Karoline VILLINGER, Britta RENNER, Harald REITERER, Daniel A. KEIM, 2019. SMARTexplore : Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach. IEEE Conference on Visual Analytics Science and Technology (VAST) 2018. Berlin, Germany, 21. Okt. 2018 - 26. Okt. 2018. In: IEEE Conference on Visual Analytics Science and Technology (VAST) 2018. Piscataway, NJ: IEEE, 2019. ISBN 978-1-5386-6861-0. Available under: doi: 10.1109/VAST.2018.8802486
BibTex
@inproceedings{Blumenschein2019SMART-43582,
  year={2019},
  doi={10.1109/VAST.2018.8802486},
  title={SMARTexplore : Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach},
  isbn={978-1-5386-6861-0},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={IEEE Conference on Visual Analytics Science and Technology (VAST) 2018},
  author={Blumenschein, Michael and Behrisch, Michael and Schmid, Stefanie and Butscher, Simon and Wahl, Deborah R. and Villinger, Karoline and Renner, Britta and Reiterer, Harald and Keim, Daniel A.}
}
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/123456789/43582">
    <dc:contributor>Wahl, Deborah R.</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Blumenschein, Michael</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Butscher, Simon</dc:creator>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/43582/3/Blumenschein_2-mv29yhuqzckr3.pdf"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Wahl, Deborah R.</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-10-18T13:58:29Z</dcterms:available>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:contributor>Behrisch, Michael</dc:contributor>
    <dc:creator>Behrisch, Michael</dc:creator>
    <dc:contributor>Renner, Britta</dc:contributor>
    <dc:creator>Reiterer, Harald</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:language>eng</dc:language>
    <dc:contributor>Reiterer, Harald</dc:contributor>
    <dc:creator>Renner, Britta</dc:creator>
    <dc:contributor>Schmid, Stefanie</dc:contributor>
    <dc:creator>Schmid, Stefanie</dc:creator>
    <dcterms:abstract xml:lang="eng">We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst’s trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing highdimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.</dcterms:abstract>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/43582"/>
    <dc:contributor>Villinger, Karoline</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-10-18T13:58:29Z</dc:date>
    <dc:rights>terms-of-use</dc:rights>
    <dc:creator>Blumenschein, Michael</dc:creator>
    <dc:creator>Villinger, Karoline</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/43582/3/Blumenschein_2-mv29yhuqzckr3.pdf"/>
    <dcterms:issued>2019</dcterms:issued>
    <dc:contributor>Butscher, Simon</dc:contributor>
    <dcterms:title>SMARTexplore : Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach</dcterms:title>
  </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