Analysis of Distributed Incomplete Pattern Matching

ABSTRACT In this paper we present a novel Unsupervised Weighted Bloom Filter (UWBF) method, to perform incomplete pattern matching in distributed environment. Various pattern matching Techniques like Naive method; Bloom Filter and Weighted Bloom Filter perform pattern matching. In existing methods, global data from all the nodes is collected to the data centre, which conducts their aggregation and then applies the pattern matching. But this approach results into high communicationcost and traffic in network. In UWBF Data-Centre distributes data to various Base-Stations and then perform pattern matching. The problem of traffic in network is automatically solved. UWBF perform pattern matching by taking untrained patterns, so accuracy increases and it requires less time than previous methods. Compare and contrast of above techniques are discussed.