Efficient Entity Maching over Multiple Data Sources with MapReduce

The execution of data-intensive tasks such as entity matching on large data sources has become a common demand in the era of Big Data. To face this challenge, cloud computing has proven to be a powerful ally to efficient parallel the execution of such tasks. In this work we investigate how to efficiently perform entity matching over multiple large data sources using the MapReduce programming model. We propose MSBlockSlicer, a MapReduce-based approach that supports blocking techniques to reduce the entity matching search space. The approach utilizes a preprocessing MapReduce job to analyze the data distribution and provides an improved load balancing by applying an efficient block slice strategy as well as a well-known optimization algorithm to assign the generated match tasks. We evaluate our approach against an existing one that addresses the same problem on a real cloud infrastructure. The results show that our approach increases significantly the performance of distributed entity match tasks by reducing the amount of data generated from the map phase and minimizing the execution time.