Similarity Search: The Metric Space Approach

ZEZULA, Pavel, Giuseppe AMATO and Vlastislav DOHNAL. Similarity Search: The Metric Space Approach. 2007th ed. Seoul, Korea: ACM, 2007. ACM SAC 2007 Conference. ISBN 1-59593-480-4.

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

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Other information

Language

English

Type of outcome

Audiovizuální tvorba

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

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

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Abstract

V originále

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.

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.

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Links

GP201/07/P240, research and development project	Name: Distribuované indexační struktury pro podobnostní hledání Investor: Czech Science Foundation, Distributed Index Structures for Similarity Searching
1ET100300419, research and development project	Name: Inteligentní modely, algoritmy, metody a nástroje pro vytváření sémantického webu Investor: Academy of Sciences of the Czech Republic, Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)