Efficient mining of frequent target episodes from complex event sequences

Yu Feng Lin, Pei Wen Jiang, Vincent Shin-Mu Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Mining frequent episodes from event sequences is an important topic in data mining fields with wide applications. Most of the existing researches focused on mining frequent episodes from a single event sequence. However, sequences containing simultaneous events are frequently encountered and we refer to such sequences as complex event sequences. Moreover, for some practical applications, users are often interested in target episodes where the last event of an episode is the target event type. In this paper, we address the problem of mining frequent target episodes in complex event sequences. We first extend the state-of-the-art algorithm PPS to be PPS+, which serves as a basic method for mining episodes from complex event sequences. Then, we propose a novel algorithm named TEM-SES (Target Episode Mining using Simultaneous Events Set) to overcome the drawback of PPS+. Experimental evaluation demonstrates that the proposed TEM-SES algorithm outperforms PPS+ substantially in terms of execution time and memory consumption.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
EditorsWilliam Cheng-Chung Chu, Stephen Jenn-Hwa Yang, Han-Chieh Chao
PublisherIOS Press
Number of pages10
ISBN (Electronic)9781614994831
StatePublished - 1 Jan 2015
EventInternational Computer Symposium, ICS 2014 - Taichung, Taiwan
Duration: 12 Dec 201414 Dec 2014

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


ConferenceInternational Computer Symposium, ICS 2014


  • Episode mining
  • complex event sequences
  • simultaneous events
  • target events

Fingerprint Dive into the research topics of 'Efficient mining of frequent target episodes from complex event sequences'. Together they form a unique fingerprint.

Cite this