"Batko, Michal" . "Proceedings of the 2009 Second International Workshop on Similarity Search and Applications" . "Metric Index: An Efficient and Scalable Solution for Similarity Search" . "Prague, Czech Republic" . . . . . . . . "[7BB6F05C1F48]" . . "Washington, DC, USA" . . "Metric space as a universal and versatile model of similarity can be applied in various areas of non-text information retrieval. However, a general, efficient and scalable solution for metric data management is still a resisting research challenge. We introduce a novel indexing and searching mechanism called Metric Index (M-Index), that employs practically all known principles of metric space partitioning, pruning and filtering. The heart of the M-Index is a general mapping mechanism that enables to actually store the data in well-established structures such as the B+-tree or even in a distributed storage. We have implemented the M-Index with B+-tree and performed experiments on a combination of five MPEG-7 descriptors in a database of hundreds of thousands digital images. The experiments put under test several M-Index variants and compare them with two orthogonal approaches - the PM-Tree and the iDistance."@en . "Nov\u00E1k, David" . "Metric space as a universal and versatile model of similarity can be applied in various areas of non-text information retrieval. However, a general, efficient and scalable solution for metric data management is still a resisting research challenge. We introduce a novel indexing and searching mechanism called Metric Index (M-Index), that employs practically all known principles of metric space partitioning, pruning and filtering. The heart of the M-Index is a general mapping mechanism that enables to actually store the data in well-established structures such as the B+-tree or even in a distributed storage. We have implemented the M-Index with B+-tree and performed experiments on a combination of five MPEG-7 descriptors in a database of hundreds of thousands digital images. The experiments put under test several M-Index variants and compare them with two orthogonal approaches - the PM-Tree and the iDistance." . "2"^^ . . . . "P(GA201/09/0683)" . "2"^^ . "Metric Index: An Efficient and Scalable Solution for Similarity Search"@en . "RIV/00216224:14330/09:00029661" . . "9"^^ . . "2009-01-01+01:00"^^ . "IEEE Computer Society" . "978-0-7695-3765-8" . . "326014" . "14330" . . "RIV/00216224:14330/09:00029661!RIV10-GA0-14330___" . "metric space; similarity search; data structure; approximation; scalability"@en . "Metric Index: An Efficient and Scalable Solution for Similarity Search" . "Metric Index: An Efficient and Scalable Solution for Similarity Search"@en . . . .