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Similarity Search: The Metric Space Approach

česky | in English

ZEZULA, Pavel, Giuseppe AMATO and Vlastislav DOHNAL. Similarity Search: The Metric Space Approach. 2007. vyd. Seoul, Korea: ACM, 2007. ACM SAC 2007 Conference. ISBN 1-59593-480-4.
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Basic information
Original name Similarity Search: The Metric Space Approach
Name in Czech Podobnostní hledání: Pohled metrického prostoru
Authors ZEZULA, Pavel (203 Czech Republic, guarantor), Giuseppe AMATO (380 Italy) and Vlastislav DOHNAL (203 Czech Republic).
Edition 2007. vyd. Seoul, Korea, ACM SAC 2007 Conference, 2007.
Publisher ACM
Other information
Original language English
Type of outcome Audiovisual works
Field of Study Computer sciences, information science, bioinformatics
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
WWW ACM SAC 2007 - Home Page
RIV identification code RIV/00216224:14330/07:00019397
Organization unit Faculty of Informatics
ISBN 1-59593-480-4
Keywords in English similarity search; approximate search; metric space; index structures; distributed index structure; scalability
Tags approximate search, DISA, distributed index structure, index structures, Metric Space, scalability, similarity search
Tags International impact
Changed by Changed by: doc. RNDr. Vlastislav Dohnal, Ph.D., učo 2952. Changed: 23. 6. 2009 12:52.
Abstract
Similarity searching has become afundamental computational task in a variety of application areas, including multimedia information retrieval, data mining, pattern recognition, machine learning, computer vision, biomedical databases, data compression and statistical data analysis. In such environments, an exact match has little meaning, and proximity/distance (similarity/dissimilarity) concepts are typically much more fruitful for searching. In this tutorial, we review the state of the art in developing similarity search mechanisms that accept the metric space paradigm. We explain the high extensibility of the metric space approach and demonstrate its capability with examples of distance functions. The efforts to further speed up retrieval are demonstrated by a class of approximated techniques and the very recent proposals of scalable and distributed structures based on the P2P communication paradigm.
Abstract (in Czech)
Similarity searching has become afundamental computational task in a variety of application areas, including multimedia information retrieval, data mining, pattern recognition, machine learning, computer vision, biomedical databases, data compression and statistical data analysis. In such environments, an exact match has little meaning, and proximity/distance (similarity/dissimilarity) concepts are typically much more fruitful for searching. In this tutorial, we review the state of the art in developing similarity search mechanisms that accept the metric space paradigm. We explain the high extensibility of the metric space approach and demonstrate its capability with examples of distance functions. The efforts to further speed up retrieval are demonstrated by a class of approximated techniques and the very recent proposals of scalable and distributed structures based on the P2P communication paradigm.
Links
GP201/07/P240, research and development projectName: Distribuované indexační struktury pro podobnostní hledání
Investor: Czech Science Foundation, Postdoctoral projects
1ET100300419, research and development projectName: Inteligentní modely, algoritmy, metody a nástroje pro vytváření sémantického webu
Investor: Academy of Sciences of the Czech Republic, Information society (National programme of research)
PrintDisplayed: 20. 10. 2018 21:25

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