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

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.

Keywords

Multiple Stream Data;Interval Event;Weight;Significant Temporal Relation;Association Rules

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, "Approximate Frequency 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 over Data Streams," In Proc. of Research Issues in Data Engineering: Stream Data Mining and Applications 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 Sequential Patterns Across Data Streams," Univ. of nd mont Computer Science Technical Report(CS-05-04), 2005.
  10. S. Laxman, P. S. Sastry, and K. Unnikrishnan, "Discovering Frequent Generalized Episodes where Events Persist for Different Durations," IEEE Transactions on Knowledge and Data Engineering, 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, "Mining Association Rules on Significant Rare Data Using Relative Support," Journal of Systems and 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 about Temporal Intervals," Communications of the ACM, Vol.26, pp.832-843, 1983. https://doi.org/10.1145/182.358434