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


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.


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


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