Summary
Query processing over the Internet involving multiple data sources has been proven one of the most difficult and important problems in modern e-data sharing society. In this new data processing environment, three major factors affect the cost of a query: network congestion situation, server states (server workload), and data/query complexity. In this paper, we construct cost models for estimating the cost of query and split query cost into data searched cost and data transmitted cost. We also study how to capture the changes of the query system in order to update the cost models whenever it needs, and use a real discrete fourier transform method to filter the noise in the main trend of the network and the query system for the more accurate cost models. So we can choose the best query plan according to the updated cost model.
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References
Adali S., Candan K.S., Papakonstantinou Y., Subrahmanian V.S, Query caching and optimization in distributed mediator systems, Proc. of ACM SIGMOD’96, 1996, pp.137–148
Chatterjee S., Price B., Regression Analysis by Example John Wiley & Sons, 1991
Du W., Query optimization in heterogeneous DBMS, Proc. of VLDB’92, 1992, pp. 103–119
Liu W., Liao Z., Jun H., Query cost estimation through remote server analysis over the Internet, Proc. Of WI’03, 2003, pp.345–355
Gruser J.R., Raschid L, Zadorozhny V., Zhan T., Learning response time for web-sources using query feedback and application in query optimization, VLDB Journal, 9(1), 2000, pp. 18–37
Muralikrishna M., Dewitt D.J., Equi-depth histograms for estimating selectivity factors for multi-dimensional queries, Proc. of SIGMOD’88, 1988, pp.28–36.
Roth M.T., Ozcan F., Haas L.M, Cost models DO matter: providing cost information for diverse data sources in a federated system, Proc. of VLDB’99, 1999, pp. 599–610.
Ling Y., Sun W, A supplement to sampling-based methods for query size estimation in a database system SIGMOD Record, 21(4), 1992, pp.12–15
Zadorozhny V., Raschid L., Zhan T., Bright L, Validating an Access Cost Model for Wide Area Applications. Cooperative Information Systems, Cooperative Information Systems, Vol 9, 2001, pp.371–385
Zhu Q., Larson, P.A, Query Sampling Method of Estimating Local Cost Parameters in a Multidatabase System, Proc. of ICDE’94, 1994, pp. 144–153
Zhu Q., Larson P., Building Regression Cost Models for Multidatabase Systems, Proc. PDIS’96, 1996, pp. 220–231.
Zhu Q., Motheramgari S., Sun Y, Cost estimation for large queries via fractional analysisand probabilistic approach in dynamic multidatabase environments, Proc. of DEXA’00, 2000, pp.509–525
Zhu Q., Motheramgari S., Sun Y, Developing cost models with qualitative variables for dynamic multidatabase environments, Proc. of ICDE’00, 2000.
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© 2005 Springer-Verlag Berlin Heidelberg
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Liao, Z., Wang, H., Glass, D., Guo, G. (2005). Query Cost Model Constructed and Analyzed in a Dynamic Environment. In: Monitoring, Security, and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32370-8_25
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DOI: https://doi.org/10.1007/3-540-32370-8_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23245-2
Online ISBN: 978-3-540-32370-9
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