Approximate Similarity Search in Metric Data by Using Region Proximity

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F00%3A00002860" target="_blank" >RIV/00216224:14330/00:00002860 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Approximate Similarity Search in Metric Data by Using Region Proximity
Original language description
The problem of approximated similarity search for the range and nearest neighbor queries is investigated for generic metric spaces. The search speedup is achieved by ignoring data regions with a small, user defined, proximity with respect to the query. For zero proximity, exact similarity search is performed. The problem of proximity of metric regions is explained and a probabilistic approach is applied. Approximated algorithms use a small amount of auxiliary data that can easily be maintained in main memory. The idea is implemented in a metric tree environment and experimentally evaluated on real-life files using specific performance measures. Improvements of two orders of magnitude can be achieved for moderately approximated search results. It is also demonstrated that the precision of proximity measures can significantly influence the quality of approximated algorithms.
Czech name
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Czech description
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Publication year
2000
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the First DELOS Network of Excellence Workshop on "Information Seeking, Searching and Querying in Digital Libraries"
ISBN
ERCIM-01-W01
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
ERCIM
Place of publication
Zurich
Event location
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Event date
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Type of event by nationality
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UT code for WoS article
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