The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Discovering Temporal Relation Considering the Weight of Events in Multidimensional Stream Data Environment

다차원 스트림 데이터 환경에서 이벤트 가중치를 고려한 시간 관계 탐사

  • 김재인 (전남대학교 전자컴퓨터공학과) ;
  • 김대인 (전남대학교 전자컴퓨터공학과) ;
  • 송명진 (전남대학교 전자컴퓨터공학과) ;
  • 한대영 (전남대학교 전자컴퓨터공학과) ;
  • 황부현 (전남대학교 전자컴퓨터공학과)
  • Published : 2010.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

An event means a flow which has a time attribute such as a symptom of patient. Stream data collected by sensors can be summarized as an interval event which has a time interval between the start-time point and the end-time point in multiple stream data environment. Most of temporal mining techniques have considered only the frequent events. However, these approaches may ignore the infrequent event even if it is important. In this paper, we propose a new temporal data mining that can find association rules for the significant temporal relation based on interval events in multidimensional stream data environment. Our method considers the weight of events and stream data on the sensing time point of abnormal events. And we can discover association rules on the significant temporal relation regardless of the occurrence frequency of events. The experimental analysis has shown that our method provide more useful knowledge than other conventional methods.

이벤트는 환자의 증상과 같은 시간 속성을 갖는 흐름을 의미하며 센서를 통하여 수집된 스트림 데이터는 시작과 종료 시점을 갖는 인터벌 이벤트로 요약 가능하다. 그러나 대부분의 시간 마이닝 기법은 빈발 이벤트만을 고려하며, 빈발하지 않는 이벤트는 중요하더라도 제외되는 문제가 있다. 이 논문에서는 다차원 스트림 데이터 환경에서 인터벌 이벤트에 기초하여 의미있는 시간 관계에 대한 연관 규칙 마이닝 기법을 제안한다. 제안 방법은 이벤트 가중치와 이상 이벤트가 감지된 시점의 스트림 데이터만 고려하여 이벤트의 발생 횟수에 상관없이 의미있는 시간 관계에 대한 연관 규칙을 탐사한다. 그리고 성능 평가를 통하여 제안 방법이 기존의 방법에 비하여 보다 유용한 지식을 탐사함을 보인다.

Keywords

References

  1. M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy, "Mining Data Streams: A Review," SIGMOD Record, Vol.34, No.2, pp.18-26, 2005. https://doi.org/10.1145/1083784.1083789
  2. G. S. Manku and R. Motwani, "ApproximateFrequency Counts over Data Streams," In Proc.of Very Large Data Bases, pp.346-357, 2002.
  3. D. Kim, P. Park, H. Kim, and B. Hwang, "Mining Association Rules in Multidimensional Stream Data," Journal of Korea Information Processing Society, Vol.13-D, No.6, pp.765-774, 2006. https://doi.org/10.3745/KIPSTD.2006.13D.6.765
  4. D. Kim, P. Park, and B. Hwang, "Mining Association Rule for the Abnormal Event in Data Stream Systems," Journal of Korea Information Processing Society, Vol.14-D, No.5, pp.483-490, 2007. https://doi.org/10.3745/KIPSTD.2007.14-D.5.483
  5. H. Li, S. Lee, and M. Shan, "Online Mining(Recently) Maximal Frequent Itemsets overData Streams," In Proc. of Research Issues inData Engineering: Stream Data Mining andApplications 2005, pp.11-18, 2005.
  6. D. Han, D. Kim, J. Kim, C. Na, and B. Hwang, "A Method for Mining Interval Event Association Rules from a Set of Events Having Time Property," Journal of Korea Information Processing Society, Vol.16-D, No. 2, pp.185-190, 2009. https://doi.org/10.3745/KIPSTD.2009.16-D.2.185
  7. Y. Lee, J. Lee, D. Chai, B. Hwang, and K. Ryu, "Mining Temporal Interval Relational Rules from Temporal Data," The Journal of Systems and Software, Vol.82, No.1, pp.155-167, 2009. https://doi.org/10.1016/j.jss.2008.07.037
  8. J. Pei, J. Han, B. M. Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M. Hsu, "Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach," IEEE Transactions on Knowledge and Data Engineering, Vol.16, No.11, 2004. https://doi.org/10.1109/TKDE.2004.77
  9. G. Chen, X. Wu, and X. Zhu, "Mining SequentialPatterns Across Data Streams," Univ. of ndmont Computer Science TechnicalReport(CS-05-04), 2005.
  10. S. Laxman, P. S. Sastry, and K. Unnikrishnan,"Discovering Frequent Generalized Episodeswhere Events Persist for Different Durations,"IEEE Transactions on Knowledge and DataEngineering, Vol.19, No.9, pp.1188-1201, 2007. https://doi.org/10.1109/TKDE.2007.1055
  11. H. Yun, D. Ha, B. Hwang, and K. Ryu, "MiningAssociation Rules on Significant Rare DataUsing Relative Support," Journal of Systemsand Software, Vol.67, No.3, pp.181-191, 2003. https://doi.org/10.1016/S0164-1212(02)00128-0
  12. R. J. Swargam, and M. J. Palakal, "The role of least frequent item sets in association discovery," In Proc. of International Conference on Digital Information Management 2007, Vol.1, pp.217-223, 2007. https://doi.org/10.1109/ICDIM.2007.4444226
  13. J. Allen, "Maintaining Knowledge aboutTemporal Intervals," Communications of theACM, Vol.26, pp.832-843, 1983. https://doi.org/10.1145/182.358434