基于非易失性存储器的高效磨损均衡频繁模式挖掘
An Efficient and Wear-Leveling-Aware Frequent-Pattern Mining on Non-Volatile Memory
摘要：
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而不是持久存储器（PM）设计的，这可能导致严重的性能和能量开销，这是由于DRAM和PM在PM上运行时完全不同的特性。在本文中，我们提出了一个有效的和磨损均衡意识的频繁模式挖掘计划，WFPM，来解决这个问题。基于不同应用场景的真实数据集，通过一系列实验对所提出的WFPM进行了评估，WFPM实现了32.0%的性能提升，并将头表的NVM寿命提高了7.4倍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
