2007
Similarity Search: The Metric Space Approach
ZEZULA, Pavel, Giuseppe AMATO a Vlastislav DOHNALZákladní údaje
Originální název
Similarity Search: The Metric Space Approach
Název česky
Podobnostní hledání: Pohled metrického prostoru
Autoři
ZEZULA, Pavel (203 Česká republika, garant), Giuseppe AMATO (380 Itálie) a Vlastislav DOHNAL (203 Česká republika)
Vydání
2007. vyd. Seoul, Korea, ACM SAC 2007 Conference, 2007
Nakladatel
ACM
Další údaje
Jazyk
angličtina
Typ výsledku
Audiovizuální tvorba
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Česká republika
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Kód RIV
RIV/00216224:14330/07:00019397
Organizační jednotka
Fakulta informatiky
ISBN
1-59593-480-4
Klíčová slova anglicky
similarity search; approximate search; metric space; index structures; distributed index structure; scalability
Štítky
Příznaky
Mezinárodní význam
Změněno: 23. 6. 2009 12:52, doc. RNDr. Vlastislav Dohnal, Ph.D.
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.
Česky
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.
Návaznosti
| GP201/07/P240, projekt VaV |
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| 1ET100300419, projekt VaV |
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