Journal of East China Normal University(Natural Science) >

2016 , Vol. 2016 >Issue 5: 45 - 55

DOI: https://doi.org/10.3969/j.issn.1000-5641.2016.05.006

Data correlation-based partition approach for distributed deputy database

  • WANG Min ,
  • PENG Cheng-chen ,
  • LI Rong-rong ,
  • PENG Yu-wei
Expand
  • Computer School, Wuhan University, Wuhan 430072, China

Received date: 2016-06-24

  Online published: 2016-11-29

Fold

Abstract

Object deputy database (ODDB) is an advanced database system with strong ability of complex information processing. With the rapid development of data, distributed storage becomes more and more important to ODDB. However, there exist correlations between objects in ODDB, which makes the traditional data portioning method of distributed storage unsuitable. To solve this problem, we propose a data correlation-based partition approach for ODDB. Firstly, we cluster correlated objects according to the deputy tree, and each object cluster is considered as a heap file in storage. Secondly, on the basis of schema feature and semantic feature, we divide object clusters into k subsets using k-means, each subset is stored on one of the storage nodes. Finally, we compare our method with random distributed storage, the results show that our approach is obviously better in query efficiency.

Key words： distributed; object deputy database; correlation; data partition; object cluster

Cite this article

WANG Min , PENG Cheng-chen , LI Rong-rong , PENG Yu-wei . Data correlation-based partition approach for distributed deputy database[J]. Journal of East China Normal University(Natural Science), 2016 , 2016(5) : 45 -55 . DOI: 10.3969/j.issn.1000-5641.2016.05.006

References

[ 1 ] PENG Z Y, KAMBAYASHI Y. Deputy mechanisms for object-oriented database[C]//Proceedings of the 11th International Conference on Data Engineering. 1995: 333-340.
[ 2 ] KAMBAYASHI Y, PENG Z Y. Object deputy model and its applications[C]//Proceedings of the 4th Inernational Conference on Database Systems for Advenced Applications. 1995: 1-15.
[ 3 ] 彭智勇, 黄泽谦,刘俊. 基于对象代理数据库的微生物信息服务系统[J]. 计算机应用,2010(1): 5-9.
[ 4 ] PENG Z Y, SHI Y, ZHAI B X. Realization of biological data management by object deputy database system[C]//Transaction on Computational Systems Biology V. Berlin: Springer, 2006: 49-67.
[ 5 ] 彭智勇, 彭煜玮, 翟博譞. 一个基于对象代理模型的多表现地信息系统[J]. 计算机应用, 2006(9):2016-2019.
[ 6 ] 彭智勇. Web数据管理系统: 201010140168.4[P]. 2010-09-15.
[ 7 ] MACQUEEN J. Some methods for classification and analysis of multivariate observations [C]//Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. 1967: 281-297.
[ 8 ] ?黄泽谦. 对象代理数据库聚簇策略与查询优化技术研究[D].武汉: 武汉大学, 2011.
[ 9 ] BAIAO F, MATTOSO M, ZAVERUCHA G. A distribution design methodology for object DBMS[J]. Distributed and Parallel Databases, 2004,16(1): 45-90.
[10] OZSU M T, VALDURIEZ P. Principles of Distributed Database Systems[M]. New York: Springer-Verlag, 2002.
[11] NAVATHE S B, RA M. Vertical partitioning for database design: A graphical algorithm[J]. ACM Sigmod Record, 1989, 18(2): 440-450.
[12] EZEIFE C, BARKER K. A comprehensive approach to horizontal class fragmentation in a distributed object based system[J]. International Journal of Distributed and Parallel Databases, 1995(3): 247-272.
[13] ?施源, 彭智勇, 庄继峰, 等. 对象关系数据库中OID回收机制[J].计算机科学, 2004, 31(10): 566-568+581.
[14] SHAMEEM M U S, FERDOUS R. An efficient k-means algorithm integrated with Jaccard distance measure for document clustering[C]//Proceedings of the Asian Himalayas International Conference on Internet. 2009: 1-6.
[15] WEIL S A. Ceph: Reliable, scalable, and high-performance distributed storage[D]. Santa Cruz: University of California Santa Cruz, 2007.

Options
Outlines

/

〈 〉