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Large-Scale Frequent Episode Mining from Complex Event Sequences with Hierarchies

文献类型：期刊论文

作者Ao, Xiang1,2; Shi, Haoran3; Wang, Jin4; Zuo, Luo1,2; Li, Hongwei1,2; He, Qing1,2
刊名ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
出版日期2019-08-01
卷号10期号:4页码:26
关键词Frequent episode mining peak episode miner large-scale sequence mining hierarchy-aware maximal/closed episode
ISSN号2157-6904
DOI10.1145/3326163
英文摘要Frequent Episode Mining (FEM), which aims at mining frequent sub-sequences from a single long event sequence, is one of the essential building blocks for the sequence mining research field. Existing studies about FEM suffer from unsatisfied scalability when faced with complex sequences as it is an NP-complete problem for testing whether an episode occurs in a sequence. In this article, we propose a scalable, distributed framework to support FEM on "big" event sequences. As a rule of thumb, "big" illustrates an event sequence is either very long or with masses of simultaneous events. Meanwhile, the events in this article are arranged in a predefined hierarchy. It derives some abstractive events that can form episodes that may not directly appear in the input sequence. Specifically, we devise an event-centered and hierarchy-aware partitioning strategy to allocate events from different levels of the hierarchy into local processes. We then present an efficient special-purpose algorithm to improve the local mining performance. We also extend our framework to support maximal and closed episode mining in the context of event hierarchy, and to the best of our knowledge, we are the first attempt to define and discover hierarchy-aware maximal and closed episodes. We implement the proposed framework on Apache Spark and conduct experiments on both synthetic and real-world datasets. Experimental results demonstrate the efficiency and scalability of the proposed approach and show that we can find practical patterns when taking event hierarchies into account.
资助项目National Key Research and Development Program of China[2017YFB1002104] ; National Natural Science Foundation of China[U1811461] ; National Natural Science Foundation of China[61602438] ; National Natural Science Foundation of China[91846113] ; National Natural Science Foundation of China[61573335] ; CCF-Tencent Rhino-Bird Young Faculty Open Research Fund[RAGR20180111] ; Ant Financial through the Ant Financial Science Funds for Security Research ; Youth Innovation Promotion Association CAS
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000496750900004
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/14788]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Jin
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Univ Calif Irvine, Dept Comp Sci, G302 C Student Ctr, Irvine, CA 92697 USA
4.Univ Calif Los Angeles, Comp Sci Dept, 3551 Boelter Hall, Los Angeles, CA 90095 USA
推荐引用方式
GB/T 7714		 Ao, Xiang,Shi, Haoran,Wang, Jin,et al. Large-Scale Frequent Episode Mining from Complex Event Sequences with Hierarchies[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2019,10(4):26.
APA Ao, Xiang,Shi, Haoran,Wang, Jin,Zuo, Luo,Li, Hongwei,&He, Qing.(2019).Large-Scale Frequent Episode Mining from Complex Event Sequences with Hierarchies.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,10(4),26.
MLA Ao, Xiang,et al."Large-Scale Frequent Episode Mining from Complex Event Sequences with Hierarchies".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 10.4(2019):26.

入库方式： OAI收割

来源：计算技术研究所

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