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In this paper, we study the problem of approximate containment similaritysearch. Given two records Q and X, the containment similarity between Q and Xwith respect to Q is |Q intersect X|/ |Q|. Given a query record Q and a set ofrecords S, the containment similarity search finds a set of records from Swhose containment similarity regarding Q are not less than the given threshold.This problem has many important applications in commercial and scientificfields such as record matching and domain search. Existing solution relies onthe asymmetric LSH method by transforming the containment similarity towell-studied Jaccard similarity. In this paper, we use a different framework bytransforming the containment similarity to set intersection. We propose a novelaugmented KMV sketch technique, namely GB-KMV, which is data-dependent and canachieve a good trade-off between the sketch size and the accuracy. We provide aset of theoretical analysis to underpin the proposed augmented KMV sketchtechnique, and show that it outperforms the state-of-the-art technique LSH-E interms of estimation accuracy under practical assumption. Our comprehensiveexperiments on real-life datasets verify that GB-KMV is superior to LSH-E interms of the space-accuracy trade-off, time-accuracy trade-off, and the sketchconstruction time. For instance, with similar estimation accuracy (F-1 score),GB-KMV is over 100 times faster than LSH-E on some real-life dataset.

GB-KMV: An Augmented KMV Sketch for Approximate Containment Similarity Search