Quantitative Assessment Algorithm for the Dynamic Change of Data Set Based on Bloom Filter Distance
A Bloom filter is a simple space-efficient randomized data structure for representing a data set in order to support membership queries with allowable errors. Bloom filters hash each element of the set to a vector of indices using a group of hash functions, and can filter out effectively any element that does not belong to the set. It is widely used in databases, networks and distributed systems. This paper first focuses on Bloom filter vector series analysis. The Bloom filter distance is also defined. Based on statistics analysis of the dynamic change of data set to the bloom filter, a quantitative assessment algorithm for the dynamic change of data set based on bloom filter distance (QABFD) is presented. Experimental and theoretical results show that the QABFD has 99% evaluation accuracy rate.