Towards the efficient parallelization of multi-pass adaptive blocking for entity matching

Modern parallel computing programming models, such as MapReduce (MR), have proven to be powerful tools for efficient parallel execution of data-intensive tasks such as Entity Matching (EM) in the era of Big Data. For this reason, studies about challenges and possible solutions of how EM can benefit from this well-known cloud computing programming model have become an important demand nowadays. Furthermore, the effectiveness and scalability of MR-based implementations for EM depend on how well the workload distribution is balanced among all reduce tasks. In this article, we investigate how MapReduce can be used to perform efficient (load balanced) parallel EM using a variation of the multi-pass Sorted Neighborhood Method (SNM) that uses a varying size (adaptive) window. We propose Multi-pass MapReduce Duplicate Count Strategy (MultiMR-DCS++), a MR-based approach for multi-pass adaptive SNM, aiming to increase even more the performance of the SNM. The evaluation results based on real-world datasets and cluster infrastructure show that our approach increases the performance of MapReduce-based SNM regarding the EM execution time and detection quality.