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Indexing Nearest Neighbor Queries
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi.
2010 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
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.

sted, utgiver, år, opplag, sider
2010.
Serie
IT ; 10 017
Identifikatorer
URN: urn:nbn:se:uu:diva-129464OAI: oai:DiVA.org:uu-129464DiVA, id: diva2:343908
Uppsök
Technology
Veileder
Examiner
Tilgjengelig fra: 2010-08-16 Laget: 2010-08-16 Sist oppdatert: 2010-08-16bibliografisk kontrollert

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