Optimizing Bloom Filter Settings in Peer-to-Peer Multikeyword Searching

Peer-to-Peer multikeyword searching requires distributed intersection/union operations across wide area networks, raising a large amount of traffic cost. Existing schemes commonly utilize Bloom Filters (BFs) encoding to effectively reduce the traffic cost during the intersection/union operations. In this paper, we address the problem of optimizing the settings of a BF. We show, through mathematical proof, that the optimal setting of BF in terms of traffic cost is determined by the statistical information of the involved inverted lists, not the minimized false positive rate as claimed by previous studies. Through numerical analysis, we demonstrate how to obtain optimal settings. To better evaluate the performance of this design, we conduct comprehensive simulations on TREC WT10G test collection and query logs of a major commercial web search engine. Results show that our design significantly reduces the search traffic and latency of the existing approaches.