Mining frequent time interval-based event with duration patterns from temporal database

Kuan Ying Chen, Bijay Prasad Jaysawal, Jen Wei Huang, Yong Bin Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Time interval-based pattern mining is proposed to improve the lack of the information of time intervals by sequential pattern mining. Previous works of time interval-based pattern mining focused on the relations between events without considering the duration of each event. However, the same event with different time durations will cause definitely different results. For example, if some people cough for one week, they may get a cold for a while. In contrast, if some patients cough for one year, they may get pneumonia in the future. In this work, we propose two algorithms, SARA and SARS, to extract the frequent Time Interval-based Event with Duration, TIED, patterns. TIED patterns not only keep the relations between two events but also reveal the time periods when each event happens and ends. In the experiments, we propose a naive algorithm and modify a previous algorithm to compare the performances with SARA and SARS. The experimental results show that SARA and SARS are more efficient in execution time and memory usage than other two algorithms.

Original languageEnglish
Title of host publicationDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
EditorsGeorge Karypis, Longbing Cao, Wei Wang, Irwin King
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages548-554
Number of pages7
ISBN (Electronic)9781479969913
DOIs
Publication statusPublished - 2014 Mar 10
Event2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014 - Shanghai, China
Duration: 2014 Oct 302014 Nov 1

Publication series

NameDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics

Other

Other2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014
CountryChina
CityShanghai
Period14-10-3014-11-01

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management

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