Making In-Memory Frequent Pattern Mining Durable and Energy Efficient

Lin, Yi; Huang, Po‐Chun; Liu, Duo; Xiao, Zhu; Liang, Liang

doi:10.1109/icpp.2016.13

Cited by 6 publications

(3 citation statements)

References 31 publications

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Section: Introductionmentioning

confidence: 99%

“…Data mining is a highlighted technology to reveal the valuable trends behind the large datasets [1,2]. The frequent-pattern mining is an active area of data mining, which is used to identify the frequent-occurring itemsets and patterns in a given dataset [1,3]. Several prefix-tree based approaches, such as AFPIM [4], CFP-tree [5], CATS tree [6], CanTree [7], and CP-tree [8], have been proposed to optimize the mining tree structure for both space efficiency and performance.…”

Section: Introductionmentioning

confidence: 99%

“…However, they have intrinsic drawbacks in write activities [11]. To our best knowledge, there are only a few NVMoriented frequent-pattern mining methods, such as EvFP-tree [1,12] and the parallel version of EvFP-tree (PevFP-tree) [13], designed for NVMs. In detail, EvFP-tree proposed the MBA encoding scheme and Lazy Counter to reduce the bits change of Header table while reducing the writes of support counter of the FP-tree nodes.…”

Section: Introductionmentioning

confidence: 99%

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

Dong,

Zhang,

Yang

et al. 2020

Preprint

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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 7.4× over the EvFP-Tree.

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