The KIPS Transactions:PartD (정보처리학회논문지D)

Korea Information Processing Society (한국정보처리학회)
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Migration Method for Efficient Management of Temporal Data

시간지원 데이터의 효율적인 관리를 위한 이동 방법

  • Published : 2001.10.01
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Abstract

In this paper we proposed four data migration methods based on time segmented storage structure including past segment, current segment, and future segment. The migration methods proposed in this paper are the Time Granularity migration method, the LST-GET (Least valid Start Time-Greatest valid End Time) migration method, the AST-AET (Average valid Start Time-Average valid End Time) migration method, and the Min-Overlap migration method. In the each data migration method we define the dividing criterion among segments and entity versions to store on each segment. We measured the response time of queries for the proposed migration methods. When there are no LLTs (Long Lived Tuples), the average response time of AST-AET migration method and LST-GET migration method are smaller than that of Time Granularity migration method. In case of existing LLT, the performance of the LST-GET migration method decreased. The AST-AET migration method resulted in better performance for queries than the Time Granularity migration method and the LST-GET migration method. The Min-Overlap migration method resulted in the almost equal performance for queries compared with the AST-AET migration method, in case of storage utilization more efficient than the AST-AET.

본 논문에서는 시간지원 데이터를 과거 세그먼트, 현재 세그먼트, 그리고 미래 세그먼트로 분리한 저장 구조를 기반으로 하는 네 가지 데이터 이동 방법을 제안하였다. 제안한 데이터 이동 방법은 시간단위에 의한 이동 방법, LST-GET(Least valid Start Time-Greatest valid End Time)에 의한 이동 방법, AST-AET(Average valid Start Time-Average valid End Time)에 의한 이동 방법, 그리고 Min-Overlap에 의한 이동 방법이 있다. 각각의 이동 방법에서는 세그먼트의 경계값, 각 세그먼트에 저장되는 개체 버전 등을 정의하였다. 제안한 이동 방법에 대해서 사용자 질의에 대한 평균 응답 시간을 측정하였다. 실험결과, LLT(Long Lived Tuples)가 없는 경우에는 LST-GET에 의한 이동 방법, 그리고 AST-AET에 의한 이동 방법이 시간단위에 의한 이동 방법보다 성능이 우수하였다. LLT가 있는 경우에는 LST-GET에 의한 이동 방법의 성능이 저하되었다. AST-AET에 의한 이동 방법은 시간단위에 의한 이동 방법과 LST-GET에 의한 이동 방법보다 질의에 대한 성능이 우수하였다. Min-Overlap에 의한 이동 방법은 질의에 대한 평균 응답 시간에서 AST-AET에 의한 이동 방법과 비슷한 결과를 보였고, 공간 이용율 측면에서는 AST-AET에 의한 이동 방법보다 효율적이었다.

Keywords

References

  1. G. Ozsoyoglu and R. T. Snodgrass, 'Temporal and Realime Databases : A Survey,' IEEE Transactions on Knowledge and Data Engineering, Vol.7, No.4, pp.511-532, August, 1995 https://doi.org/10.1109/69.404027
  2. C. S. Jensen and R. T. Snodgrass. 'Temporal Data Management,' Technical Report (TR-17), TimeCenter, June, 1997
  3. V. Kouramajian, 'Temporal Databases: Access Structures, Search Methods, Migration Strategies, and Declustering Techniques,' Ph. D. Dissertation, The University of Texas at Arlington, 1994
  4. G. Ozsoyoglu and R. T. Snodgrass, 'Temporal and Realime Databases : A Survey,' IEEE Transactions on Knowledge and Data Engineering, Vol.7, No.4, pp.511-532, August, 1995 https://doi.org/10.1109/69.404027
  5. T. Zurek. 'Optimal Interval Partitioning for Temporal Databases,' Proceedings of The 3rd Basque International Workshop on Information Technology(BIWIT'97), pp.140-147, July, 1997 https://doi.org/10.1109/BIWIT.1997.614061
  6. J. Kim and M. Kim, 'On Effective Data Clustering in Bitemporal Databases,' Fourth International Workshop on Temporal Representation and Reasoning (TIME-97), pp.54-61, Florida, May, 1997
  7. I. Ahn and R. T. Snodgrass, 'Partitioned Storage for Temporal Databases,' Information Systems, Vol.13, No.4, pp.369-391, 1988 https://doi.org/10.1016/0306-4379(88)90004-X
  8. A. Tansel, J. Clifford, S. Gadia, S. Jajodia, A. Segev, and R. T. Snodgrass(eds.), Temporal Databases : Theory, Design, and Implementation, pp.137-138, Benjamin/cummings, Redwood City, CA, 1994
  9. H. Yun and K. Kim, 'Experimenting with Segmentation and Non-segmentation Methods for Storing Temporal Data,' Proceeding of CNDS'98, pp.113-118, San Diego, California, January, 1998
  10. I. Ahn and R. T. Snodgrass, 'Partitioned Storage for Temporal Databases,' Information Systems, Vol.13, No.4, pp.369-391, 1988 https://doi.org/10.1016/0306-4379(88)90004-X
  11. M. H. Bohlen, R. Busatto, and C. S. Jensen, 'Point versus Interval-Based Temporal Data Models,' Proceedings of 14th International Conference on Data Engineering, pp.192-200, Orlando, Florida, February, 1998
  12. K. Norvag, 'The Persistent Cache: Improving OID Indexing in Temporal Object-Oriented Database Systems,' Proceedings of the 25th International Conference on VLDB, pp.66-77, Scotland, UK, September, 1999
  13. B. Moon, I. Lopez, and V. Immanuel, 'Scalable Algorithm for Large Temporal Aggregation,' 16th International Conference on Data Engineering, pp.145-156, San Diego, California, March, 2000
  14. J. Linan, S. Betty, L. David, and B. Manuel, 'The BT-tree : A Branch and Temporal Access Method,' Proceedings of the 26th International Conference On VLDB, pp.451-460, Cairo, Egypt, September, 2000
  15. J. Lee and R. Elrnasri, 'A Temporal Algebra for an ER-Based Temporal Data Model,' Proceedings of 17th International Conference on Data Engineering, pp.33-40, Heidelberg, Germany, April 2001
  16. S. Giedrius, S. J. Christian, R. T. Snodgrass, 'Adaptable Query Optimization and Evaluation in Temporal Middleware,' Proceedings of the 2001 ACM SIGMOD, pp.127-138, Santa Barbara, California, May, 2001