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@inproceedings{558457,
author = {Batko, Michal and Gennaro, Claudio and Zezula, Pavel},
address = {Toronto},
booktitle = {2nd International VLDB Workshop on Databases, Information Systems and Peer-to-Peer Computing},
keywords = {distributed data; scalable structures; similarity search; nearest neighbors},
language = {eng},
location = {Toronto},
pages = {64-77},
publisher = {VLDB Publishing},
title = {A Scalable Nearest Neighbor Search in P2P Systems},
year = {2004}
}
TY - JOUR
ID - 558457
AU - Batko, Michal - Gennaro, Claudio - Zezula, Pavel
PY - 2004
TI - A Scalable Nearest Neighbor Search in P2P Systems
PB - VLDB Publishing
CY - Toronto
KW - distributed data
KW - scalable structures
KW - similarity search
KW - nearest neighbors
N2 - Similarity search in metric spaces represents an important paradigm for content-based retrieval in many applications. Existing centralized search structures can speed-up retrieval, but they do not scale up to large volume of data because the response time is linearly increasing with the size of the searched file. In this article, we study the problem of executing the nearest neighbor(s) queries in a distributed metric structure, which is based on the P2P communication paradigm and the generalized hyperplane partitioning. By exploiting parallelism in a dynamic network of computers, the query execution scales up very well considering both the number of distance computations and the hop count between the peers. Results are verified by experiments on real-life data sets.
ER -
BATKO, Michal, Claudio GENNARO a Pavel ZEZULA. A Scalable Nearest Neighbor Search in P2P Systems. In \textit{2nd International VLDB Workshop on Databases, Information Systems and Peer-to-Peer Computing}. Toronto: VLDB Publishing, 2004, s.~64-77.
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