Entity resolution acceleration using the automata processor

Entity Resolution (ER), the process of finding identical entities across different databases, is critical to many information-integration applications. As sizes of databases explode in the big-data era, it becomes computationally expensive to recognize identical entities among all records with variations allowed across multiple databases. Profiling results show that approximate matching is the primary bottleneck. The Automata Processor (AP), an efficient and scalable semiconductor architecture for parallel automata processing, provides a new opportunity for hardware acceleration for ER. We propose an AP-accelerated ER solution, which accelerates the performance bottleneck of fuzzy matching for similar but potentially inexactly-matched names, and use several different real-world applications to illustrate its effectiveness. We compared the proposed method with several conventional methods and achieved both promising speedups and better accuracy (more correct pairs and less generalized merge distance cost) for different datasets.