Mining Precise-Positioning Episode Rules from Event Sequences

doi:10.1109/TKDE.2017.2773493

	Mining Precise-Positioning Episode Rules from Event Sequences
	Ao, Xiang 1,2; Luo, Ping 1,2; Wang, Jin 3; Zhuang, Fuzhen 1,2; He, Qing 1,2
	2018-03-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	30 期号:3 页码:530-543
摘要	Episode Rule Mining is a popular framework for discovering sequential rules from event sequential data. However, traditional episode rule mining methods only tell that the consequent event is likely to happen within a given time interval after the occurrence of the antecedent events. As a result, they cannot satisfy the requirement of many time sensitive applications, such as program security trading and intelligent transportation management due to the lack of fine-grained response time. In this study, we come up with the concept of fixed-gap episode to address this problem. A fixed-gap episode consists of an ordered set of events where the elapsed time between any two consecutive events is a constant. Based on this concept, we formulate the problem of mining precise-positioning episode rules in which the occurrence time of each event in the consequent is clearly specified. In addition, we develop a trie-based data structure to mine such precise-positioning episode rules with several pruning strategies incorporated for improving the performance as well as reducing memory consumption. Experimental results on real datasets show the superiority of our proposed algorithms.
关键词	Episode rule mining gap-constrained episode sequence mining
DOI	10.1109/TKDE.2017.2773493
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61602438] ; National Natural Science Foundation of China[91546122] ; National Natural Science Foundation of China[61573335] ; National Natural Science Foundation of China[61473274] ; National Natural Science Foundation of China[61773361] ; National Natural Science Foundation of China[61473273] ; National Key R & D Program of China[2017YFB1002104] ; Guangdong Provincial Science and Technology Plan Projects[2015 B010109005]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000424637500010
出版者	IEEE COMPUTER SOC
引用统计	被引频次：35[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/5651
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ao, Xiang
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Univ Calif Los Angeles, Comp Sci Dept, Los Angeles, CA 90095 USA
推荐引用方式 GB/T 7714	Ao, Xiang,Luo, Ping,Wang, Jin,et al. Mining Precise-Positioning Episode Rules from Event Sequences[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2018,30(3):530-543.
APA	Ao, Xiang,Luo, Ping,Wang, Jin,Zhuang, Fuzhen,&He, Qing.(2018).Mining Precise-Positioning Episode Rules from Event Sequences.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,30(3),530-543.
MLA	Ao, Xiang,et al."Mining Precise-Positioning Episode Rules from Event Sequences".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 30.3(2018):530-543.