Hierarchical Pattern Mining with the Automata Processor

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Abstract

Mining complex patterns with hierarchical structures becomes more and more important to understand the underlying information in large and unstructured databases. When compared with a set-mining problem or a string-mining problem, the computation complexity to recognize a pattern with hierarchical structure, and the large associated search space, make hierarchical pattern mining (HPM) extremely expensive on conventional processor architectures. We propose a flexible, hardware-accelerated framework for mining hierarchical patterns with Apriori-based algorithms, which leads to multi-pass pruning strategies but exposes massive parallelism. Under this framework, we implemented two widely used HPM techniques, sequential pattern mining (SPM) and disjunctive rule mining (DRM) on the Automata Processor (AP), a hardware implementation of non-deterministic finite automata (NFAs). Two automaton-design strategies for matching and counting different types of hierarchical patterns, called linear design and reduction design, are proposed in this paper. To generalize automaton structure for SPM, the linear design strategy is proposed by flattening sequential patterns to plain strings to produce automaton design space and to minimize the overhead of reconfiguration. Up to 90\(\times \) and 29\(\times \) speedups are achieved by the AP-accelerated algorithm on six real-world datasets, when compared with the optimized multicore CPU and GPU GSP implementations, respectively. The proposed CPU-AP solution also outperforms the state-of-the-art PrefixSpan and SPADE algorithms on a multicore CPU by up to 452\(\times \) and 49\(\times \) speedups. The AP advantage grows further with larger datasets. For DRM, the reduction design strategy is adopted by applying reduction operation of AND, with on-chip Boolean units, on several parallel sub-structures for recognizing disjunctive items. This strategy allows implicit OR reduction on alternative items within a disjunctive item by utilizing bit-wise parallelism feature of the on-chip state units. The experiments show up to 614\(\times \) speedups of the proposed CPU-AP DRM solution over a sequential CPU algorithm on two real-world datasets. The experiments also show significant increase of CPU matching-and-counting time when increasing d-rule size or the number of alternative items. However, in practical cases, the AP solution runs hundreds of times faster in matching and counting than the CPU solution, and keeps constant processing time despite the increasing complexity of disjunctive rules.

Keywords

Data mining Automata Processor Sequential pattern mining Disjunctive rule mining Finite automaton 

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of VirginiaCharlottesvilleUSA

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