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Frequent-pattern mining is a common approach to reveal the valuable hiddentrends behind data. However, existing frequent-pattern mining algorithms aredesigned for DRAM, instead of persistent memories (PMs), which can lead tosevere performance and energy overhead due to the utterly differentcharacteristics between DRAM and PMs when they are running on PMs. In thispaper, we propose an efficient and Wear-leveling-aware Frequent-Pattern Miningscheme, WFPM, to solve this problem. The proposed WFPM is evaluated by a seriesof experiments based on realistic datasets from diversified applicationscenarios, where WFPM achieves 32.0% performance improvement and prolongs theNVM lifetime of header table by 7.4x over the EvFP-Tree.

An Efficient and Wear-Leveling-Aware Frequent-Pattern Mining on Non-Volatile Memory