Browse > Article

KDBcs-Tree : An Efficient Cache Conscious KDB-Tree for Multidimentional Data  

Yeo, Myung-Ho (충북대학교 정보통신공학과)
Min, Young-Soo (한국전자통신연구원 홈네트워크연구단)
Yoo, Jae-Soo (충북대학교 전기전자컴퓨터공학부)
Publication Information
Journal of KIISE:Databases / v.34, no.4, 2007 , pp. 328-342 More about this Journal
Abstract
We propose a new cache conscious indexing structure for processing frequently updated data efficiently. Our proposed index structure is based on a KDB-Tree, one of the representative index structures based on space partitioning techniques. In this paper, we propose a data compression technique and a pointer elimination technique to increase the utilization of a cache line. To show our proposed index structure's superiority, we compare our index structure with variants of the CR-tree(e.g. the FF CR-tree and the SE CR-tree) in a variety of environments. As a result, our experimental results show that the proposed index structure achieves about 85%, 97%, and 86% performance improvements over the existing index structures in terms of insertion, update and cache-utilization, respectively.
Keywords
Cache-miss; Update Performance; Index Structure; KDB-tree;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Jun Rao and Kenneth A. Ross, 'Making B+-Trees Cache Conscious in Main Memory,' Proceedings of the ACM SIGMOD Conference, pp.475-486, 2000   DOI   ScienceOn
2 Anastassia Ailamaki, David J. DeWitt, Mark D. Hill and David A. Wood, 'DBMSs On A Modern Processor: Where Does Time Go?,' Proceedings of the 25th VLDB Conference, pp.266-277, 1999
3 Ratko Orlandic, Byunggu Yu, 'Implementing KDBTrees to Support High-Dimensional Data,' Proceedings of the International Database Engineering & Applications Symposium, IEEE, pp.58-67, 2001
4 Kihong Kim, Sang K. Cha and Keunjoo Kwon, 'Optimizing Multidimensional Index Trees for Main Memory Access,' Proceedings of the ACM SIGMOD Conference, pp.139-150, 2001   DOI   ScienceOn
5 R. Orlandic and B. Yu, 'Estimating the Probability of Overlap between Multi-imensional Rectangles in the Analysis of Spatial Structures,' Information Sciences, 2001
6 John T. Robinson, 'The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes,' Proceedings of the ACM SIGMOD Conference, pp.10-18, 1981
7 S.T. Leutenegger, 'Multi Dimensional Data Sets,' http://www.cs.du.edu/~leut/MultiDimData.html, 1996
8 Kaushik Chakrabarti and Sharad Mehrotra, 'The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces,' Proceedings of the International Conference on Data Engineering, pp. 440-447, 1999
9 Phil Bernstain. et al., 'The Asilomar report on database research,' Sigmod Record, 27(4), 1998
10 Peter A. Boncz, et al., 'Database architecture optimized for the new bottleneck: Memory access,' Proceedings of the 25th VLDB Conference, pp.54-65, 1999
11 Byunggu Yu, Tomas Bailey, Ratko Orlandic, Jothi Somavaram, 'KDBKD-Tree: A Compact KDB-Tree Structure for Indexing Multidimensional Data,' Proceedings of the International Conference on Information Technology: Coding and Computing[Computers and Communications], IEEE, pp.676-680, 2003
12 A. Guttman, 'R-trees: A Dynamic Index Structure for Spatial Searching,' Proceedings of ACM SIGMOD Conference, pp.47-57, 1984
13 Jun Rao and Kenneth A. Ross, 'Cache Conscious Indexing for Decision-Support in Main Memory,' Proceedings of the 25th VLDB Conference, pp.78-89, 1999