The pivot tables is one of the most effective metric access method optimized for a number of distance computations in similarity search. In this work the new modification of the pivot tables method was proposed that is besides distance computations optimized also for a number of I/O operations. Proposed Clustered pivot tables method is indexing clusters of similar objects that were created by another metric access method - the M-tree. The indexing of clustered objects has a positive effect for searching within indexed database. Whereas the clusters are paged in second memory, page containing such cluster, which do not satisfy particular query, is not accessed in second memory at all. Non-relevant objects, that are out of the query range, are not loaded into memory, what has the effect of decreasing number of I/O operations and total volume of transferred data. The correctness of proposed approach was experimentally proved and experimental results of proposed method was compared to selected metric access methods.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:295917 |
| Date | January 2011 |
| Creators | Moško, Juraj |
| Contributors | Skopal, Tomáš, Hoksza, David |
| Source Sets | Czech ETDs |
| Language | Slovak |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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