Adaptive Approximate Similarity Searching through Metric Social Networks
| Authors | |
|---|---|
| Year of publication | 2007 |
| Type | R&D Presentation |
| MU Faculty or unit | |
| Citation | |
| Description | Exploiting the concepts of social networking represents a novel approach to the approximate similarity query processing. We present an unstructured and dynamic P2P environment in which a metric social network is built. Social communities of peers giving similar results to specific queries are established and such ties are exploited for answering future queries. Based on the universal law of generalization, a new query forwarding algorithm is introduced and evaluated. The same principle is used to manage query histories of individual peers with the possibility to tune the tradeoff between the extent of the history and the level of the query-answer approximation. All proposed algorithms are tested on real data and medium-sized P2P networks consisting of tens of computers. |
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