NÁLEPA, Filip, Michal BATKO and Pavel ZEZULA. Combining Cache and Priority Queue to Enhance Evaluation of Similarity Search Queries. Online. In Maozhen Li, Xiong Ning, Zheng Xiao, Guoqing Xiao, Kenli Li, and Lipo Wang. 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Neuveden: IEEE, 2018, p. 956-963. ISBN 978-1-5386-8097-1. Available from: https://dx.doi.org/10.1109/FSKD.2018.8687208.
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Basic information
Original name Combining Cache and Priority Queue to Enhance Evaluation of Similarity Search Queries
Authors NÁLEPA, Filip (203 Czech Republic, guarantor, belonging to the institution), Michal BATKO (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution).
Edition Neuveden, 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, p. 956-963, 8 pp. 2018.
Publisher IEEE
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14330/18:00101090
Organization unit Faculty of Informatics
ISBN 978-1-5386-8097-1
Doi http://dx.doi.org/10.1109/FSKD.2018.8687208
Keywords in English approximate similarity search; multiple kNN queries; data partitions caching; priority queue based similarity search
Tags DISA, firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Filip Nálepa, Ph.D., učo 359760. Changed: 16/4/2019 20:34.
Abstract
A variety of applications have been using content-based similarity search techniques. Higher effectiveness of the search can be, in some cases, achieved by submitting multiple similar queries. We propose new approximation techniques that are specially designed to enhance the trade-off between the effectiveness and the efficiency of multiple k-nearest-neighbors queries. They combine the probability of an indexed object to be a part of the precise query result and the time needed to examine the object. This enables us to improve processing times while maintaining the same query precision as compared to the traditional approximation technique without the proposed optimizations.
Links
GA16-18889S, research and development projectName: Analytika pro velká nestrukturovaná data (Acronym: Big Data Analytics for Unstructured Data)
Investor: Czech Science Foundation
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