Frequent-pattern mining is a common approach to reveal the valuable hidden trends behind data. However, existing frequent-pattern mining algorithms are designed for DRAM, instead of persistent memories (PMs), which can lead to severe performance and energy overhead due to the utterly different characteristics between DRAM and PMs when they are running on PMs. In this paper, we propose an efficient and Wear-leveling-aware Frequent-Pattern Mining scheme, WFPM, to solve this problem. The proposed WFPM is evaluated by a series of experiments based on realistic datasets from diversified application scenarios, where WFPM achieves 32.0% performance improvement and prolongs the NVM lifetime of header table by 7.4x over the EvFP-Tree.

中文翻译：

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非易失性存储器上高效且具有磨损均衡意识的频繁模式挖掘

频繁模式挖掘是揭示数据背后有价值的隐藏趋势的常用方法。然而，现有的频繁模式挖掘算法是为 DRAM 而不是持久性存储器 (PM) 设计的，由于 DRAM 和 PM 在 PM 上运行时完全不同的特性，这会导致严重的性能和能源开销。在本文中，我们提出了一种高效且具有磨损均衡意识的频繁模式挖掘方案 WFPM 来解决此问题。所提出的 WFPM 通过基于来自不同应用场景的真实数据集的一系列实验进行评估，其中 WFPM 实现了 32.0% 的性能提升，并将头表的 NVM 寿命延长了 EvFP-Tree 的 7.4 倍。