中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Mining Precise-Positioning Episode Rules from Event Sequences

文献类型:期刊论文

作者Ao, Xiang1,2; Luo, Ping1,2; Wang, Jin3; Zhuang, Fuzhen1,2; He, Qing1,2
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2018-03-01
卷号30期号:3页码:530-543
关键词Episode rule mining gap-constrained episode sequence mining
ISSN号1041-4347
DOI10.1109/TKDE.2017.2773493
英文摘要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.
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
语种英语
WOS记录号WOS:000424637500010
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/5650]  
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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, 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.

入库方式: OAI收割

来源:计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。