Frequent subtree mining on the automata processor
Elaheh Sadredini, Reza Rahimi, Ke Wang, Kevin Skadron · 2017
Frequency counting of complex patterns such as subtrees is more challenging than for simple itemsets and sequences, as the number of possible candidate patterns in a tree is much higher than one-dimensional data structures, with dramatically higher processing times. In this paper, we propose a new and scalable solution for frequent subtree mining (FTM) on the Automata Processor (AP), a new and highly parallel accelerator architecture. We present a multi-stage pruning framework on the AP, called AP-FTM, to reduce the search space of FTM candidates. This achieves up to 353X speedup at the cost of a small reduction in accuracy, on four real-world and synthetic datasets, when compared with PatternMatcher, a practical and exact CPU solution. To provide a fully accurate and still scalable solution, we propose a hybrid method to combine AP-FTM with a CPU exact-matching approach, and achieve up to 262X speedup over PatternMatcher on a challenging database. We also develop a GPU algorithm for FTM, but show that the AP also outperforms this. The results on a synthetic database show the AP advantage grows further with larger datasets.