Scalable frequent-pattern mining on nonvolatile memories

摘要

Frequent-pattern mining is a common means to reveal the hidden trends behind data. However, most frequent-pattern mining algorithms are designed for DRAM, instead of the energy-economic nonvolatile memories (NVMs). Due to the huge differences between the characteristics of NVMs and those of DRAM, existing frequent-pattern mining algorithms suffer from serious overheads of write amplification or energy consumption as used on NVMs. The design complexity is exaggerated when parallel computing is used to speedup the mining process. This paper proposes PevFP-tree, a parallel frequent-pattern mining solution for NVMs, e.g., phase-change memory (PCM). By considering the NVM characteristics, PevFP-tree accelerates the mining process and enhance the energy efficiency. Moreover, PevFP-tree offers superior scalability in terms of the degree of parallelism of the mining algorithm and the branching factor of its tree structure. The efficacy of PevFP-tree is evaluated by experiments based on realistic datasets.