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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...
Brill tagging is a classic rule-based algorithm for part-of-speech (POS) tagging that assigns tags, such as nouns, verbs, adjectives, etc., to input tokens. Due to the the intense memory requirements of rule matching, CPU implementations of the Brill tagging algorithm have been found to be slow. We show that Micron's Automata Processor (AP) — a new computing architecture that can perform massively...
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...
Determining the effect of a compound on IKr is a standard screen for drug safety. Often the effect is described using a single IC50 value, which is unable to capture complex effects of a drug. Using verapamil as an example, we present a method for using recordings from native myocytes at several drug doses along with qualitative features of IKr from published studies of HERG current to estimate...
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