Query processing with quality control in the World Wide Web
World Wide Web
Developing Evolutionary Cost Models for Query Optimization in a Dynamic Multidatabase Environment
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
Knowledge and Information Systems
Optimizing Recursive Information Gathering Plans in EMERAC
Journal of Intelligent Information Systems
A Frequency-based Approach for Mining Coverage Statistics in Data Integration
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Evolutionary techniques for updating query cost models in a dynamic multidatabase environment
The VLDB Journal — The International Journal on Very Large Data Bases
Effectively Mining and Using Coverage and Overlap Statistics for Data Integration
IEEE Transactions on Knowledge and Data Engineering
Statistical learning techniques for costing XML queries
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Query cost estimation through remote system contention states analysis over the Internet
Web Intelligence and Agent Systems
Distributed and Parallel Databases
Optimizing recursive information gathering plans
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
Active and accelerated learning of cost models for optimizing scientific applications
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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A major challenge for performing global query optimization in a multidatabase system (MDBS) is the lack of cost models for local database systems at the global level. In this paper we present a statistical procedure based on multiple regression analysis for building cost models for local database systems in an MDBS. Explanatory variables that can be included in a regression model are identified and a mixed forward and backward method for selecting significant explanatory variables is presented. Measures for developing useful regression cost models, such as removing outliers, eliminating multicollinearity, validating regression model assumptions, and checking significance of regression models, are discussed. Experimental results demonstrate that the presented statistical procedure can develop useful local cost models in an MDBS.