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Demand-Driven Caching in Multiuser Environment
January 2004 (vol. 16 no. 1)
pp. 112-124
ASCII Text
x 
 
Shen-Tat Goh, Beng Chin Ooi, Kian-Lee Tan, "Demand-Driven Caching in Multiuser Environment," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 1, pp. 112-124, January, 2004.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2004.1264826,
author = {Shen-Tat Goh and Beng Chin Ooi and Kian-Lee Tan},
title = {Demand-Driven Caching in Multiuser Environment},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {16},
number = {1},
issn = {1041-4347},
year = {2004},
pages = {112-124},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2004.1264826},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Demand-Driven Caching in Multiuser Environment
IS - 1
SN - 1041-4347
SP112
EP124
EPD - 112-124
A1 - Shen-Tat Goh,
A1 - Beng Chin Ooi,
A1 - Kian-Lee Tan,
PY - 2004
KW - Cache-on-demand
KW - predictive
KW - virtual cache
KW - return on investment.
VL - 16
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Kian-Lee Tan, IEEE Computer Society

Abstract—In this paper, we propose a novel demand-driven caching framework, called cache-on-demand (CoD). In CoD, intermediate/final answers of existing running queries are viewed as virtual caches that can be materialized if they are beneficial to incoming queries. Such an approach is essentially nonspeculative: the exact cost of investment and the return on investment are known, and the cache is certain to be reused! We address several issues for CoD to be realized. We also propose three optimizing strategies: Conform-CoD, Scramble-CoD, and Integrated-CoD. Conform-CoD and Scramble-CoD are based on a two-phase optimization framework, while Integrated-CoD operates in a single-phase framework. We conducted extensive performance study to evaluate the effectiveness of these algorithms. Our results show that all the CoD-based schemes can provide substantial performance improvement when compared with a predictive scheme and a no-caching scheme.

[1] S. Chaudhuri, R. Krishnamurthy, S. Potamianos, and K. Shim, "Optimizing Queries with Materialized Views," Proc. 11th Int'l Conf. Data Eng.,Taipei, Taiwan, 1995.
[2] C. Chen and N. Roussopoulos, The Implementation and Performance Evaluation of the Adms Query Optimizer: Integrating Query Result Caching and Matching Proc. Int'l Conf. Extending Data Base Technology, pp. 323-336, Mar. 1994.
[3] N. Dalvi, S. Sanghai, P. Roy, and S. Sudarshan, Pipelining in Multi-Query Optimization Proc. 2001 Symp. Principles of Database Systems (PODS '01), May 2001.
[4] P. Deshpande and J. Naughton, Aggregate Aware Caching for Multi-Dimensional Queries Proc. Int'l Conf. Extending Data Base Technology, pp. 167-182, Mar. 2000.
[5] P. Deshpande, K. Ramasamy, A. Shukla, and J. Naughton, Caching Multidimensional Queries Using Chunks Proc. 1998 ACM-SIGMOD Int'l Conf. Management of Data, pp. 259-270, June 1998.
[6] Y. Ioannidis and Y. Kang, Randomized Algorithms for Optimizing Large Join Queries Proc. 1990 ACM-SIGMOD Int'l Conf. Management of Data, pp. 312-321, May 1990.
[7] D. Kossmann, M. Franklin, G. Drasch, and W. Ag, Cache Investment: Integrating Query Optimization and Dynamic Data Placement ACM Trans. Database Systems, vol. 25, no. 4, pp. 517-558, 2000.
[8] A. Rosenthal and U.S. Chakravarthy, Anatomy of a Modular Multiple Query Optimizer Proc. 14th Int'l Conf. Very Large Data Bases, pp. 230-239, Aug. 1988.
[9] P. Roy, K. Ramamritham, S. Seshadri, P. Shenoy, and S. Sudarshan, Don't Trash Your Intermediate Results, Cache 'em CoRR (Number 0003005), Mar. 2000.
[10] P. Roy, S. Seshadri, S. Sudarshan, and S. Bhobe, Efficient and Extensible Algorithms for Multi-Query Optimization Proc. 2000 ACM-SIGMOD Int'l Conf. Management of Data, pp. 249-260, June 2000.
[11] T. Sellis, Multiple Query Optimization ACM Trans. Databases, vol. 13, no. 1, pp. 23-52, Mar. 1988.
[12] J. Shim, P. Scheuermann, and R. Vingralek, Dynamic Caching of Query Results for Decision Support Systems Proc. Int'l Conf. Scientific and Statistical Databases, pp. 254-263, July 1999.
[13] S.N. Subramanian and S. Venkataraman, Cost-Based Optimization of Decision Support Queries Using Transient Views Proc. 1998 ACM-SIGMOD Int'l Conf. Management of Data, pp. 319-330, June 1998.
[14] K.L. Tan and H. Lu, Workload Scheduling of Multi-Join Queries Information Processing Letters, vol. 55, no. 5, pp. 251-257, 1995.

Index Terms:
Cache-on-demand, predictive, virtual cache, return on investment.
Citation:
Shen-Tat Goh, Beng Chin Ooi, Kian-Lee Tan, "Demand-Driven Caching in Multiuser Environment," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 1, pp. 112-124, Jan. 2004, doi:10.1109/TKDE.2004.1264826
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