Mining Precise-Positioning Episode Rules from Event Sequences

• Published in: 2017 IEEE 33rd International Conference on Data Engineering (ICDE) pp. 83 - 86
• Main Authors: Xiang Ao, Ping Luo, Jin Wang, Fuzhen Zhuang, Qing He
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2017
• Subjects: Data mining; Data structures; Finite element analysis; Partitioning algorithms; Registers; Security; Time factors
• ISSN: 2375-026X
• DOI: 10.1109/ICDE.2017.45

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 intervals 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 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 triebased 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.