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@inproceedings{1346582,
author = {Novák, David and Zezula, Pavel},
address = {Berlin Heidelberg},
booktitle = {Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIV},
doi = {http://dx.doi.org/10.1007/978-3-662-49214-7_2},
editor = {Abdelkader Hameurlain, Josef Küng, Roland Wagner, Hendrik Decker, Lenka Lhotska, Sebastian Link},
keywords = {similarity search; pivot permutations; approximate search; kNN search},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Berlin Heidelberg},
isbn = {978-3-662-49213-0},
pages = {61-87},
publisher = {Springer},
title = {PPP-Codes for Large-Scale Similarity Searching},
url = {http://link.springer.com/chapter/10.1007/978-3-662-49214-7_2},
year = {2016}
}
TY - JOUR
ID - 1346582
AU - Novák, David - Zezula, Pavel
PY - 2016
TI - PPP-Codes for Large-Scale Similarity Searching
PB - Springer
CY - Berlin Heidelberg
SN - 9783662492130
KW - similarity search
KW - pivot permutations
KW - approximate search
KW - kNN search
UR - http://link.springer.com/chapter/10.1007/978-3-662-49214-7_2
L2 - http://link.springer.com/chapter/10.1007/978-3-662-49214-7_2
N2 - Many current applications need to organize data with respect to mutual similarity between data objects. A typical general strategy to retrieve objects similar to a given sample is to access and then refine a candidate set of objects. We propose an indexing and search technique that can significantly reduce the candidate set size by combination of several space partitionings. Specifically, we propose a mapping of objects from a generic metric space onto main memory codes using several pivot spaces; our search algorithm first ranks objects within each pivot space and then aggregates these rankings producing a candidate set reduced by two orders of magnitude while keeping the same answer quality. Our approach is designed to well exploit contemporary HW: (1) larger main memories allow us to use rich and fast index, (2) multi-core CPUs well suit our parallel search algorithm, and (3) SSD disks without mechanical seeks enable efficient selective retrieval of candidate objects. The gain of the significant candidate set reduction is paid by the overhead of the candidate ranking algorithm and thus our approach is more advantageous for datasets with expensive candidate set refinement, i.e. large data objects or expensive similarity function. On real-life datasets, the search time speedup achieved by our approach is by factor of two to five.
ER -
NOVÁK, David a Pavel ZEZULA. PPP-Codes for Large-Scale Similarity Searching. In Abdelkader Hameurlain, Josef Küng, Roland Wagner, Hendrik Decker, Lenka Lhotska, Sebastian Link. \textit{Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIV}. Berlin Heidelberg: Springer, 2016, s.~61-87. ISBN~978-3-662-49213-0. Dostupné z: https://dx.doi.org/10.1007/978-3-662-49214-7\_{}2.
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