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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...
Association rule mining (ARM) is a widely used data mining technique for discovering sets of frequently associated items in large databases. As datasets grow in size and real-time analysis becomes important, the performance of ARM implementation can impede its applicability. We accelerate ARM by using Micron's Automata Processor (AP), a hardware implementation of non-deterministic finite automata...
Semantic analysis often uses a pipeline of Natural Language Processing (NLP) tools such as part-of-speech (POS) tagging. Brill tagging is a classic rule-based algorithm for POS tagging within NLP. However, implementation of the tagger is inherently slow on conventional Von Neumann architectures. In this paper, we accelerate the second stage of Brill tagging on the Micron Automata Processor, a new...
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