TY - GEN
T1 - Efficient mining of frequent target episodes from complex event sequences
AU - Lin, Yu Feng
AU - Jiang, Pei Wen
AU - Tseng, Vincent Shin-Mu
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - Episode mining
KW - complex event sequences
KW - simultaneous events
KW - target events
UR - http://www.scopus.com/inward/record.url?scp=84926476434&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-484-8-501
DO - 10.3233/978-1-61499-484-8-501
M3 - Conference contribution
AN - SCOPUS:84926476434
T3 - Frontiers in Artificial Intelligence and Applications
SP - 501
EP - 510
BT - Intelligent Systems and Applications - Proceedings of the International Computer Symposium, ICS 2014
A2 - Chu, William Cheng-Chung
A2 - Yang, Stephen Jenn-Hwa
A2 - Chao, Han-Chieh
PB - IOS Press
Y2 - 12 December 2014 through 14 December 2014
ER -