Document similarity self-join with MapReduce

Lucchese C, Baraglia R, De Francisci Morales G

Similarity Self-Join  Database Management. Data mining  Data Mining  All Pair Similarity  Parallel Algorithms 

Given a collection of objects, the Similarity Self-Join problem requires to discover all those pairs of objects whose similarity is above a user defined threshold. In this paper we focus on document collections, which are characterized by a sparseness that allows effective pruning strategies. Our contribution is a new parallel algorithm within the MapReduce framework. This work borrows from the state of the art in serial algorithms for similarity join and MapReduce-based techniques for set-similarity join. The proposed algorithm shows that it is possible to leverage a distributed file system to support communication patterns that do not naturally fit the MapReduce framework. Scalability is achieved by introducing a partitioning strategy able to overcome memory bottlenecks. Experimental evidence on real world data shows that our algorithm outperforms the state of the art by a factor 4.5.

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