Parallel meta-blocking: Realizing scalable entity resolution over large, heterogeneous data Full text

Vasilis Efthymiou, George Papadakis, George Papastefanatos, Kostas Stefanidis, Themis Palpanas
IEEE International Conference on Big Data (IEEE BigData 2015), Oct 29 - Nov 01, 2015 @ Santa Clara, CA, USA
Abstract. Entity resolution constitutes a crucial task for many applications, but has an inherently quadratic complexity. Typically, it scales to large volumes of data through blocking: similar entities are clustered into blocks so that it success to perform comparisons only within each block. Meta-blocking further increases efficiency by cleaning the overlapping blocks from unnecessary comparisons. However, even Meta-blocking can be time-consuming: applying it to blocks with 7.4 million entities and 2.2*1011 comparisons takes almost 8 days on a modern high-end server. In this paper, we parallelize Metablocking based on MapReduce. We propose a simple strategy that explicitly creates the core concept of Meta-blocking, the blocking graph. We then describe an advanced strategy that creates the blocking graph implicitly, reducing the overhead of data exchange. We also introduce a load balancing algorithm that distributes the computationally intensive workload evenly among the available compute nodes. Our experimental analysis verifies the superiority of our advanced strategy and demonstrates an almost linear speedup for all meta-blocking techniques with respect to the number of available nodes.