Journal of Software, Vol 6, No 5 (2011), 857-865, May 2011
doi:10.4304/jsw.6.5.857-865

An Efficient Method for Improving Query Efficiency in Data Warehouse

Zhiwei Ni, Junfeng Guo, Li Wang, Yazhuo Gao

Abstract


There are lots of performance bottlenecks for real-time queries in mass data. Many methods can only improve the efficiency for frequently used queries, but it is not advisable to neglect the non-frequently used queries. This paper proposes a new integrated index model called BBI and illustrates the application of this model. Based on the feature of data warehouse and OLAP queries, this index model is built with inverted index, aggregation table, bitmap index and b-tree. It greatly promotes not only the efficiency of frequently used queries, but also the performance of other queries. The analytical and experimental results demonstrate the utility of BBI.


Keywords


Aggregation Table; Inverted Index; Bitmap Index; B-Tree Index

References


[1]Joshi.S, Jermaine.C, “Materialized Sample Views for Database,” [J] IEEE Transactions on Knowledge and Data Engineering, Volume 20, Issue 3, pp: 337 – 351, March 2008.
doi:10.1109/TKDE.2007.190664

[2]Guangrong.Li, Xiaohua.Hu and etc, “A Novel Unsupervised Feature Selection Method for Bioinformatics Data Sets through Feature Clustering” Proc. Granular Computing, 2008. IEEE International Conference on 26-28 Aug. 2008(GrC 2008.), pp: 41 - 47.

[3]Dimension-Join: A New Index for Data Warehouses http://www4.wiwiss.fu-berlin.de/dblp/resource/record/conf/sbbd/BizarroM01.

[4]E. Morita, R. Sabourin, F. Bortolozzi, and C.Y. Suen, “Unsupervised Feature Selection Using Multi-Objective Genetic Algorithm for Handwritten Word Recognition”, in the 7th International Conference on Document Analysis and Recognition, Edinburgh, Scotland, 2003, pp.666-670.

[5]Gibas.M, Canahuate.G, Ferhatosmanoglu.H, “On row Index Recommendations for High-Dimensional Databases Using Query Workloads” IEEE Transactions on Knowledge and Data Engineering, Volume 20, Issue 2, Feb. 2008 Page(s):246 - 260.

[6]P•O’Neil, D•Quass. Improved query performance with variant indexes [EB/OL]. http://www.cs.duke.edu/~junyang/courses/cps216-2003-spring/papers/oneil-quass-1997.pdf,1997-05.

[7]Jingren.Zho, Larson.P.A, Goldstein.J, Luping.Ding, “Dynamic Materialized Views”, Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on 15-20 April 2007 Page(s):526 - 535.

[8]Yin.GS, Yu.X, Lin.LD, ” Strategy of Selecting Materialized Views Based on Cache updating”, IEEE International Conference on Integration Technology Shenzhen, CHINA, MAR 20-24, 2007 pp:789-792.

[9]Jeffrey.Xu.Yu, Xin.Yao, ChiHon.Choi, Gang.Gou. “Materialized View Selection as Constrained Evolutionary Optimization”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Volume 33, Issue 4, Nov. 2003 Page(s):458 - 467.

[10]H. Gupta and I. S. Mumick, “Selection of views to materialize under a maintenance cost constraint,” in Proc. 7th Int. Conf. Database Theory,1999, pp. 453–470.

[11]A. Shukla, P. Deshpande, and J. F. Naughton, Materialized view selection for multidimensional datasets,in Proc. 24th Int. Conf. Very Large Data Bases, 1998, pp. 488–499.

[12]Byeong-Seob You, Dong-Wook Lee, et al. Hybrid Index for Spatio-temporal OLAP Operations[A] //International Conference on Advances in Information Systems(ADVIS 2006). Germany:Springer,2006:110-118.

[13]Wen Juan, Xue Yongshen, et al. An Efficient Method for Multi-Table Joining in Data Warehouse[J]. Journal of Computer Research and Development, 2005, 44(11): 2010~2017(in Chinese).

[14]M. Frank, E. Omiecinski, and S. Navathe, “Adaptive and Automated Index Selection in RDBMS,” Proc. Third Int’l Conf. Extending Database Technology (EDBT ’92), 1992.

[15]S. Choenni, H. Blanken, and T. Chang, “On the Selection of Secondary Indexes in Relational Databases,” Data and Knowledge Eng., 1993.

[16]A. Capara, M. Fischetti, and D. Maio, “Exact and Approximate Algorithms for the Index Selection Problem in Physical Database Design”, Knowledge and Data Engineering, IEEE Transactions on Volume 7, Issue 6, Dec. 1995 Page(s):955 - 967.

[17]Xiaolei Li, Jiawei Han, Hector Gonzalez.High-dimensional OLAP:a minimal cubing approach[A]. NASCIMENTO M A,OZSU M T,KOSSMANN D,et al. International Conference on Very Large Data Bases(VLDB 2004).San Fransisco:Morgan Kaufmann,2004:528-539.

[18]Yan.Jun; Liu, Ning; Yan, Shuicheng; Yang, Qiang; Chen, Zheng;, “Synthesizing Novel Dimension Reduction Algorithms in Matrix Trace Oriented Optimization Framework”, Data Mining, 2009. ICDM '09. Ninth IEEE International Conference on 6-9 Dec. 2009 Page(s):598 - 606.

[19]Smalter.A, Huan.Jun, Lushington.G, “Feature Selection in the Tensor Product Feature Space”, in the ICDM '09. 2009 Page(s):1004-1009.


Full Text: PDF


Journal of Software (JSW, ISSN 1796-217X)

Copyright @ 2006-2012 by ACADEMY PUBLISHER – All rights reserved.