COCA Filters: Co-occurrence Aware Bloom Filters

We propose an indexing data structure based on a novel variation of Bloom filters. Signature files have been proposed in the past as a method to index large text databases though they suffer from a high false positive error problem. In this paper we introduce COCA Filters, a new type of Bloom filters which exploits the co-occurrence probability of words in documents to reduce the false positive error. We show experimentally that by using this technique we can reduce the false positive error by up to 21 times for the same index size. Furthermore Bloom filters can be replaced by COCA filters wherever the co-occurrence of any two members of the universe is identifiable.