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      이동체 데이터베이스를 위한 디클러스터링 정책 = Declustering Method for Moving Object Database

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      https://www.riss.kr/link?id=A101433103

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      Because there are so many spatio-temporal data in Moving Object Databases, a single disk system can not gain the fast response time and tota throughput. So it is needed to take a parallel processing system for the high effectiveness query process. In these existing parallel process-ing system. it does not consider characters of moving object data. Moving object data have to be thought about continuous report to the Moving Object Databases. So it is necessary think about the new Declustering System for the high performance system. In this paper, we propose the new Dechustering Policies of Moving objet data for high effectiveness query processing. At first, consider a spatial part of MBB(Minimum Bounding Box) then take a SD(SemiAllocation Disk) value. Second time, consider a SD value and time value which is node made at together as SDT-Proximity. And for more accuracy Declustering effect, consider a Load Balancing. Evaluation shows performance improvement of aver-age %15\%$ compare with Round-Robin method about $5\%\;and\;10\%$ query area. And performance improvement of average $6\%$ compare with Spatial Proximity method.
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      Because there are so many spatio-temporal data in Moving Object Databases, a single disk system can not gain the fast response time and tota throughput. So it is needed to take a parallel processing system for the high effectiveness query process. In ...

      Because there are so many spatio-temporal data in Moving Object Databases, a single disk system can not gain the fast response time and tota throughput. So it is needed to take a parallel processing system for the high effectiveness query process. In these existing parallel process-ing system. it does not consider characters of moving object data. Moving object data have to be thought about continuous report to the Moving Object Databases. So it is necessary think about the new Declustering System for the high performance system. In this paper, we propose the new Dechustering Policies of Moving objet data for high effectiveness query processing. At first, consider a spatial part of MBB(Minimum Bounding Box) then take a SD(SemiAllocation Disk) value. Second time, consider a SD value and time value which is node made at together as SDT-Proximity. And for more accuracy Declustering effect, consider a Load Balancing. Evaluation shows performance improvement of aver-age %15\%$ compare with Round-Robin method about $5\%\;and\;10\%$ query area. And performance improvement of average $6\%$ compare with Spatial Proximity method.

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      참고문헌 (Reference)

      1 홍은석, "이동체 데이터의 근접성을 이용한 시공간 디클러스터링 방법" 30 (30): 767 -769, 2003.

      2 홍은석, "이동체 데이터베이스에서 시공간 근접성을 고려한 디클러스터링정책" 제30권 (제30권): 118-120, 2003.

      3 Ibrahim Kamel,Christos Faloustsos, "Parallel R-Trees" 195-204, 1992.

      4 Yannis Theodoridis, "On the Generation of Spatio-temporal Datasets" 1999.

      5 Dieter Proser,, "Novel Approaches to the Indexing of Moving Object Trajectories" (1) : 395-406, 2000.

      6 Bernhard Seeger,Per-Ake Larson, "Multi-Disk B-trees" 436-445, 1991.

      7 Sanjiv Behl, "Efficient Declustering Techniques for Temporal Access Structures" 436-445, 1991.

      8 Nick Koudas, "Declustering Spatial Databases on a Multi-Computer Architecture" 592-614, 1996.

      9 HV Jagadish, "Analysis of the hilbert curve for representing two-dimensional space" 17-22, 1997.

      10 Peter J. Varman, "An Efficient Multiversion Access Structure" 391-409, 1997.

      1 홍은석, "이동체 데이터의 근접성을 이용한 시공간 디클러스터링 방법" 30 (30): 767 -769, 2003.

      2 홍은석, "이동체 데이터베이스에서 시공간 근접성을 고려한 디클러스터링정책" 제30권 (제30권): 118-120, 2003.

      3 Ibrahim Kamel,Christos Faloustsos, "Parallel R-Trees" 195-204, 1992.

      4 Yannis Theodoridis, "On the Generation of Spatio-temporal Datasets" 1999.

      5 Dieter Proser,, "Novel Approaches to the Indexing of Moving Object Trajectories" (1) : 395-406, 2000.

      6 Bernhard Seeger,Per-Ake Larson, "Multi-Disk B-trees" 436-445, 1991.

      7 Sanjiv Behl, "Efficient Declustering Techniques for Temporal Access Structures" 436-445, 1991.

      8 Nick Koudas, "Declustering Spatial Databases on a Multi-Computer Architecture" 592-614, 1996.

      9 HV Jagadish, "Analysis of the hilbert curve for representing two-dimensional space" 17-22, 1997.

      10 Peter J. Varman, "An Efficient Multiversion Access Structure" 391-409, 1997.

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      연월일 이력구분 이력상세 등재구분
      2012-10-01 평가 학술지 통합(등재유지)
      2010-01-01 평가 등재학술지 유지(등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지(등재유지) KCI등재
      2006-01-01 평가 등재학술지 유지(등재유지) KCI등재
      2003-01-01 평가 등재학술지 선정(등재후보2차) KCI등재
      2002-01-01 평가 등재후보 1차 PASS(등재후보1차) KCI등재후보
      2000-07-01 평가 등재후보학술지 선정(신규평가) KCI등재후보
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