Fast Nearest Neighbor Search in High-Dimensional Spaces

Lade...
Vorschaubild
Dateien
ICDE98.pdf
ICDE98.pdfGröße: 154.32 KBDownloads: 603
Datum
1998
Autor:innen
Berchtold, Stefan
Ertl, Bernhard
Kriegel, Hans-Peter
Seidl, Thomas
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
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
IEEE COMPUTER SOCIETY TECHNICAL COMMITTEE ON DATA ENGINEERING, , ed.. Proceedings : 14th International Conference on Data Engineering, February 23-27, 1998, Orlando, Florida. Los Alamitos: IEEE, 1998, pp. 209-218. ISBN 0-8186-8289-2
Zusammenfassung

Similarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of high-dimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore precompute the result of any nearest-neighbor search which corresponds to a computation of the voronoi cell of each data point. In a second step, we store the voronoi cells in an index structure efficient for high-dimensional data spaces. As a result, nearest neighbor search corresponds to a simple point query on the index structure. Although our technique is based on a precomputation of the solution space, it is dynamic, i.e. it supports insertions of new data points. An extensive experimental evaluation of our technique demonstrates the high efficiency for uniformly distributed as well as real data. We obtained a significant reduction of the search time compared to nearest neighbor search in the X-tree (up to a factor of 4).

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690BERCHTOLD, Stefan, Bernhard ERTL, Daniel A. KEIM, Hans-Peter KRIEGEL, Thomas SEIDL, 1998. Fast Nearest Neighbor Search in High-Dimensional Spaces. In: IEEE COMPUTER SOCIETY TECHNICAL COMMITTEE ON DATA ENGINEERING, , ed.. Proceedings : 14th International Conference on Data Engineering, February 23-27, 1998, Orlando, Florida. Los Alamitos: IEEE, 1998, pp. 209-218. ISBN 0-8186-8289-2
BibTex
@inproceedings{Berchtold1998Neare-5712,
  year={1998},
  title={Fast Nearest Neighbor Search in High-Dimensional Spaces},
  isbn={0-8186-8289-2},
  publisher={IEEE},
  address={Los Alamitos},
  booktitle={Proceedings : 14th International Conference on Data Engineering, February 23-27, 1998, Orlando, Florida},
  pages={209--218},
  editor={IEEE Computer Society Technical Committee on Data Engineering},
  author={Berchtold, Stefan and Ertl, Bernhard and Keim, Daniel A. and Kriegel, Hans-Peter and Seidl, Thomas}
}
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/123456789/5712">
    <dcterms:bibliographicCitation>First publ. in: Proceedings / 14th International Conference on Data Engineering (ICDE'98), Orlando, FL, September, 1998, pp. 209-218</dcterms:bibliographicCitation>
    <dc:language>eng</dc:language>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:59:31Z</dcterms:available>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:title>Fast Nearest Neighbor Search in High-Dimensional Spaces</dcterms:title>
    <dcterms:issued>1998</dcterms:issued>
    <dc:creator>Berchtold, Stefan</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:59:31Z</dc:date>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5712/1/ICDE98.pdf"/>
    <dc:contributor>Berchtold, Stefan</dc:contributor>
    <dc:creator>Ertl, Bernhard</dc:creator>
    <dc:format>application/pdf</dc:format>
    <dc:contributor>Ertl, Bernhard</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Kriegel, Hans-Peter</dc:contributor>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5712/1/ICDE98.pdf"/>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dcterms:abstract xml:lang="eng">Similarity search in multimedia databases requires an efficient support of nearest-neighbor search on a large set of high-dimensional points as a basic operation for query processing. As recent theoretical results show, state of the art approaches to nearest-neighbor search are not efficient in higher dimensions. In our new approach, we therefore precompute the result of any nearest-neighbor search which corresponds to a computation of the voronoi cell of each data point. In a second step, we store the voronoi cells in an index structure efficient for high-dimensional data spaces. As a result, nearest neighbor search corresponds to a simple point query on the index structure. Although our technique is based on a precomputation of the solution space, it is dynamic, i.e. it supports insertions of new data points. An extensive experimental evaluation of our technique demonstrates the high efficiency for uniformly distributed as well as real data. We obtained a significant reduction of the search time compared to nearest neighbor search in the X-tree (up to a factor of 4).</dcterms:abstract>
    <dc:contributor>Seidl, Thomas</dc:contributor>
    <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Seidl, Thomas</dc:creator>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5712"/>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/2.0/"/>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:creator>Kriegel, Hans-Peter</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
Nein
Begutachtet
Diese Publikation teilen