Abstract：Graph similarity join has extensive use in the field of data mining, especially in data pre-processing, it could be applied to data cleaning, near duplicate detection, etc. Thus, it is of great importance to study graph similarity join. Graph similarit join based on edit distance constraints is studied, that is, all the edit distances in the return pair of graphs are no larger than a given threshold. Based on MapReduce programming model, an algorithm named MGSJoin is proposed with the ″filtering-verification″ framework, and it relies on graph signatures of path-based q-grams for filtering out non-promising candidates, i.e. count filtering.With the potential issue of too many key-value pairs, Bloom Filter is introduced to improve the algorithm and BMGSJoin is designed. The improvement of efficiency and scalability by the proposed algorithm is demonstrated by extensive experimental results, and it may meet the current challenges of big data mining and analysis.
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2015, Vol. 28 
