TY - GEN
T1 - Subsequence search considering duration and relations of events in time interval-based events sequences
AU - Yang, Cheng Wei
AU - Jaysawal, Bijay Prasad
AU - Huang, Jen Wei
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Previous works of subsequence search in time interval-based events sequences have focused on the relations among events without considering the duration of each event. However, the same event with different time duration may lead to different results. In this work, we propose an index structure referred to as Endpoint Index based on the concept of inverted index to efficiently extract the Time Interval-based Event with Duration, TIED, subsequence. TIED subsequence search considers duration of event intervals in conjunction with relation among events. This makes the results of TIED subsequence search more accurate than those obtained using traditional search methods. In addition, we propose an algorithm SSD, Subsequence Search with Duration, incorporating a pruning strategy to search TIED subsequences efficiently. For the performance evaluation, we modify previous algorithms to compare with our proposed methods SSD_Endpoint and SSD_ER. The experimental results demonstrate that SSD_Endpoint and SSD_ER are more efficient than state-of-the-art algorithms.
AB - Previous works of subsequence search in time interval-based events sequences have focused on the relations among events without considering the duration of each event. However, the same event with different time duration may lead to different results. In this work, we propose an index structure referred to as Endpoint Index based on the concept of inverted index to efficiently extract the Time Interval-based Event with Duration, TIED, subsequence. TIED subsequence search considers duration of event intervals in conjunction with relation among events. This makes the results of TIED subsequence search more accurate than those obtained using traditional search methods. In addition, we propose an algorithm SSD, Subsequence Search with Duration, incorporating a pruning strategy to search TIED subsequences efficiently. For the performance evaluation, we modify previous algorithms to compare with our proposed methods SSD_Endpoint and SSD_ER. The experimental results demonstrate that SSD_Endpoint and SSD_ER are more efficient than state-of-the-art algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85046282321&partnerID=8YFLogxK
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U2 - 10.1109/DSAA.2017.47
DO - 10.1109/DSAA.2017.47
M3 - Conference contribution
T3 - Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
SP - 293
EP - 302
BT - Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Data Science and Advanced Analytics, DSAA 2017
Y2 - 19 October 2017 through 21 October 2017
ER -