Finding Pseudo Periods over Data Streams based on Multiple Hash Functions

다중 해시함수 기반 데이터 스트림에서의 아이템 의사 주기 탐사 기법

  • 이학주 (연세대학교 일반대학원 컴퓨터과학과) ;
  • 김재완 (연세대학교 일반대학원 컴퓨터과학과) ;
  • 이원석 (연세대학교 컴퓨터과학과)
  • Received : 2016.09.12
  • Accepted : 2017.02.06
  • Published : 2017.03.31


Recently in-memory data stream processing has been actively applied to various subjects such as query processing, OLAP, data mining, i.e., frequent item sets, association rules, clustering. However, finding regular periodic patterns of events in an infinite data stream gets less attention. Most researches about finding periods use autocorrelation functions to find certain changes in periodic patterns, not period itself. And they usually find periodic patterns in time-series databases, not in data streams. Literally a period means the length or era of time that some phenomenon recur in a certain time interval. However in real applications a data set indeed evolves with tiny differences as time elapses. This kind of a period is called as a pseudo-period. This paper proposes a new scheme called FPMH (Finding Periods using Multiple Hash functions) algorithm to find such a set of pseudo-periods over a data stream based on multiple hash functions. According to the type of pseudo period, this paper categorizes FPMH into three, FPMH-E, FPMH-PC, FPMH-PP. To maximize the performance of the algorithm in the data stream environment and to keep most recent periodic patterns in memory, we applied decay mechanism to FPMH algorithms. FPMH algorithm minimizes the usage of memory as well as processing time with acceptable accuracy.


Grant : 빅데이터 환경에서 비식별화 기법을 이용한 개인정보보호 기술 개발

Supported by : 정보통신기술진흥센터


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