A Scalable Nearest Neighbor Search in P2P Systems
| Authors | |
|---|---|
| Year of publication | 2004 |
| Type | Article in Proceedings |
| Conference | 2nd International VLDB Workshop on Databases, Information Systems and Peer-to-Peer Computing |
| MU Faculty or unit | |
| Citation | |
| Field | Computer hardware and software |
| Keywords | distributed data; scalable structures; similarity search; nearest neighbors |
| Description | 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. |
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