With the growing popularity of shared resources, large volumes of complex
data of different types are collected automatically. Traditional data mining
algorithms generally have problems and challenges including huge memory cost,
low processing speed, and inadequate hard disk space. As a fundamental task of
data mining, sequential pattern mining (SPM) is used in a wide variety of
real-life applications. However, it is more complex and challenging than other
pattern mining tasks, i.e., frequent itemset mining and association rule
mining, and also suffers from the above challenges when handling the
large-scale data. To solve these problems, mining sequential patterns in a
parallel or distributed computing environment has emerged as an important issue
with many applications. In this paper, an in-depth survey of the current status
of parallel sequential pattern mining (PSPM) is investigated and provided,
including detailed categorization of traditional serial SPM approaches, and
state of the art parallel SPM. We review the related work of parallel
sequential pattern mining in detail, including partition-based algorithms for
PSPM, Apriori-based PSPM, pattern growth based PSPM, and hybrid algorithms for
PSPM, and provide deep description (i.e., characteristics, advantages,
disadvantages and summarization) of these parallel approaches of PSPM. Some
advanced topics for PSPM, including parallel quantitative / weighted / utility
sequential pattern mining, PSPM from uncertain data and stream data, hardware
acceleration for PSPM, are further reviewed in details. Besides, we review and
provide some well-known open-source software of PSPM. Finally, we summarize
some challenges and opportunities of PSPM in the big data era.

本文深入调查和提供了当前并行顺序模式挖掘的现状，包括传统串行 SPM 方法的详细分类，以及最先进的并行 SPM 方法。我们详细审查了并行顺序模式挖掘的相关工作，包括基于划分的算法，基于 Apriori 的方法，基于模式增长的 PSPM 和混合算法，并深入介绍了这些并行 PSPM 方法的特点、优势、缺点和总结。此外，我们还审查了一些著名的 PSPM 开源软件。最后，我们总结了大数据时代 PSPM 的一些挑战和机遇。