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@article{905348,
author = {Novák, David and Batko, Michal and Zezula, Pavel},
article_number = {4},
doi = {http://dx.doi.org/10.1016/j.is.2010.10.002},
keywords = {Metric space; Similarity search; Data structure; Approximation; Scalability},
language = {eng},
issn = {0306-4379},
journal = {Information Systems},
title = {Metric index: an efficient and scalable solution for precise and approximate similarity search},
volume = {36},
year = {2011}
}
TY - JOUR
ID - 905348
AU - Novák, David - Batko, Michal - Zezula, Pavel
PY - 2011
TI - Metric index: an efficient and scalable solution for precise and approximate similarity search
JF - Information Systems
VL - 36
IS - 4
SP - 721-733
EP - 721-733
PB - Elsevier
SN - 03064379
KW - Metric space
KW - Similarity search
KW - Data structure
KW - Approximation
KW - Scalability
N2 - Metric space is a universal and versatile model of similarity that can be applied in various areas of 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, thus reaching high search performance while having constant building costs per object. The heart of the M-Index is a general mapping mechanism that enables to actually store the data in established structures such as the B+ - tree or even in a distributed storage. We implemented the M-Index with the B+ - tree and performed experiments on two datasets - the first is an artificial set of vectors and the other is a real-life dataset composed of a combination of five MPEG-7 visual descriptors extracted from a database of up to several million digital images. The experiments put several M-Index variants under test and compare them with established techniques for both precise and approximate similarity search. The trials show that the M-Index outperforms the others in terms of efficiency of search-space pruning, I/O costs, and response times for precise similarity queries. Further, the M-Index demonstrates excellent ability to keep similar data close in the index which makes its approximation algorithm very efficient - maintaining practically constant response times while preserving a very high recall as the dataset grows and even beating approaches designed purely for approximate search.
ER -
NOVÁK, David, Michal BATKO a Pavel ZEZULA. Metric index: an efficient and scalable solution for precise and approximate similarity search. \textit{Information Systems}. Elsevier, roč.~36, č.~4, s.~721-733. ISSN~0306-4379. doi:10.1016/j.is.2010.10.002. 2011.
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