Fast Parallel Similarity Search in Multimedia Databases

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BERCHTOLD, Stefan, Christian BÖHM, Bernhard BRAUNMÜLLER, Daniel A. KEIM, Hans-Peter KRIEGEL, 1997. Fast Parallel Similarity Search in Multimedia Databases. the 1997 ACM SIGMOD international conference. Tucson, Arizona, United States, May 11, 1997 - May 15, 1997. In: Proceedings of the 1997 ACM SIGMOD international conference on Management of data - SIGMOD '97. New York, New York, USA:ACM Press, pp. 1-12. ISBN 0-89791-911-4. Available under: doi: 10.1145/253260.253263

@inproceedings{Berchtold1997Paral-5776, title={Fast Parallel Similarity Search in Multimedia Databases}, year={1997}, doi={10.1145/253260.253263}, isbn={0-89791-911-4}, address={New York, New York, USA}, publisher={ACM Press}, booktitle={Proceedings of the 1997 ACM SIGMOD international conference on Management of data - SIGMOD '97}, pages={1--12}, author={Berchtold, Stefan and Böhm, Christian and Braunmüller, Bernhard and Keim, Daniel A. and Kriegel, Hans-Peter} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dcterms:title>Fast Parallel Similarity Search in Multimedia Databases</dcterms:title> <dc:creator>Kriegel, Hans-Peter</dc:creator> <dc:contributor>Böhm, Christian</dc:contributor> <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights> <dc:date rdf:datatype="">2011-03-24T16:00:02Z</dc:date> <dc:format>application/pdf</dc:format> <dc:creator>Berchtold, Stefan</dc:creator> <dc:contributor>Keim, Daniel A.</dc:contributor> <dcterms:rights rdf:resource=""/> <dc:contributor>Braunmüller, Bernhard</dc:contributor> <dcterms:issued>1997</dcterms:issued> <dcterms:isPartOf rdf:resource=""/> <bibo:uri rdf:resource=""/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Keim, Daniel A.</dc:creator> <dc:language>eng</dc:language> <dcterms:available rdf:datatype="">2011-03-24T16:00:02Z</dcterms:available> <dc:creator>Braunmüller, Bernhard</dc:creator> <dcterms:bibliographicCitation>First publ. in: Proceedings of the 1997 ACM SIGMOD international conference on Management of data, 1997, pp. 1-12</dcterms:bibliographicCitation> <dc:contributor>Kriegel, Hans-Peter</dc:contributor> <dc:contributor>Berchtold, Stefan</dc:contributor> <dcterms:abstract xml:lang="eng">Most similarity search techniques map the data objects into some high-dimensional feature space. The similarity search then corresponds to a nearest-neighbor search in the feature space which is computationally very intensive. In this paper, we present a new parallel method for fast nearest-neighbor search in high-dimensional feature spaces. The core problem of designing a parallel nearestneighbor algorithm is to find an adequate distribution of the data onto the disks. Unfortunately, the known declustering methods do not perform well for high-dimensional nearest-neighbor search. In contrast, our method has been optimized based on the special properties of high-dimensional spaces and therefore provides a near-optimal distribution of the data items among the disks. The basic idea of our data declustering technique is to assign the buckets corresponding to different quadrants of the data space to different disks. We show that our technique - in contrast to other declustering methods - guarantees that all buckets corresponding to neighboring quadrants are assigned to different disks. We evaluate our method using large amounts of real data (up to 40 MBytes) and compare it with the best known data declustering method, the Hilbert curve. Our experiments show that our method provides an almost linear speed-up and a constant scale-up. Additionally, it outperforms the Hilbert approach by a factor of up to 5.</dcterms:abstract> <dcterms:hasPart rdf:resource=""/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dspace:hasBitstream rdf:resource=""/> <dspace:isPartOfCollection rdf:resource=""/> <dc:creator>Böhm, Christian</dc:creator> </rdf:Description> </rdf:RDF>

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