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Indexing Nearest Neighbor Queries
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2010 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In database technology, one very well known problem is K nearest neighbor (KNN). However, the cost of finding a solution of the KNN problem may be expensive with the increase of database size. In order to achieve efficient data mining of large amounts of data, it is important to index high dimensional data to support KNN search.

Xtree, an index structure for high dimensional data, was investigated and then integrated into Amos II, an extensible functional Database Management System (DBMS). The result of the integration is AmosXtree, which has showed that the query time for KNN search on high dimensional data, is scale well with both database size and dimensionality.

To utilize the functionality of AmosXtree, an example is given on how to define an index structure in searching pictures.

Place, publisher, year, edition, pages
2010.
Series
IT ; 10 017
Identifiers
URN: urn:nbn:se:uu:diva-129464OAI: oai:DiVA.org:uu-129464DiVA, id: diva2:343908
Uppsok
Technology
Supervisors
Examiners
Available from: 2010-08-16 Created: 2010-08-16 Last updated: 2010-08-16Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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