Reverse k-Nearest Neighbor Query Processing Method for Continuous Query Processing in Bigdata Environments

빅데이터 환경에서 연속 질의 처리를 위한 리버스 k-최근접 질의 처리 기법

  • 임종태 (충북대학교 정보통신공학과) ;
  • 박선용 (충북대학교 정보통신공학과) ;
  • 서기원 (충북대학교 정보통신공학과) ;
  • 이민호 (한국과학기술정보연구원) ;
  • 복경수 (충북대학교 정보통신공학과) ;
  • 유재수 (충북대학교 정보통신공학과)
  • Received : 2014.09.01
  • Accepted : 2014.09.19
  • Published : 2014.10.28


With the development of location aware technologies and mobile devices, location-based services have been studied. To provide location-based services, many researchers proposed methods for processing various query types with Mapreduce(MR). One of the proposed methods, is a Reverse k-nearest neighbor(RkNN) query processing method with MR. However, the existing methods spend too much cost to process the continuous RkNN query. In this paper, we propose an efficient continuous RkNN query processing method with MR to resolve the problems of the existing methods. The proposed method uses the 60-degree-pruning method. The proposed method does not need to reprocess the query for continuous query processing because the proposed method draws and monitors the monitoring area including the candidate objects of a RkNN query. In order to show the superiority of the proposed method, we compare it with the query processing performance of the existing method.


MapReduce;Reverse k-NN Query;Continuous Query;Location-based Services;Bigdata


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