Efficient batch similarity join processing of soci

Authors: Yi Zhuang Nan Jiang ZhiAng Wu Jie Cao Chunhua Ju

Publish Date: 2015/07/09

Volume: 19, Issue: 4, Pages: 725-753

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Abstract

In this paper we identify and solve a multijoin optimization problem for Arbitrary Featurebased social image Similarity JOINsAFSJOIN Given two collectionsie R and S of social images that carry both visual spatial and textualie tag information the multiple joins based on arbitrary features retrieves the pairs of images that are visually textually similar or spatially close from different users To address this problem in this paper we have proposed three methods to facilitate the multijoin processing 1 two baseline approachesie a naïve join approach and a maximal thresholdMTbased and 2 a Batch Similarity JoinBSJ method For the BSJ method given m users’ join requests they are first conversed and grouped into m″ clusters which correspond to m″ join boxes where m m″ To speedup the BSJ processing a feature distance space is first partitioned into some cubes based on four segmentation schemes the image pairs falling in the cubes are indexed by the cube tree index thus BSJ processing is transformed into the searching of the image pairs falling in some affected cubes for m″ AFSJOINs with the aid of the index An extensive experimental evaluation using real and synthetic datasets shows that our proposed BSJ technique outperforms the stateoftheart solutionsThis paper is partially supported by the Program of National Natural Science Foundation of China under Grant No 61272188 the Program of Natural Science Foundation of Zhejiang Province under Grant Nos LY13F020008 and LY13F020010 the Ministry of Education of Humanities and Social Sciences Project under Grant No 14YJCZH235 National Center for International Joint Research on EBusiness Information Processing 2013B01035

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