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Power-Law Based Estimation of Set Similarity Join Size

We propose a novel technique for estimating the size of set similarity join. The proposed technique relies on a succinct representation of sets using Min-Hash signatures. We exploit frequent patterns in the signatures for the Set Similarity Join (SSJoin) size estimation by counting their support. However, there are overlaps among the counts of signature patterns and we need to use the set Inclusion-Exclusion (IE) principle. We develop a novel lattice-based counting method for efficiently evaluating the IE principle. The proposed counting technique is linear in the lattice size. To make the mining process very light-weight, we exploit a recently discovered Power-law relationship of pattern count and frequency. Extensive experimental evaluations show the proposed technique is capable of accurate and efficient estimation.