Cost Modeling and Estimation for OLAP-XML Federations
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
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
Evolutionary techniques for updating query cost models in a dynamic multidatabase environment
The VLDB Journal — The International Journal on Very Large Data Bases
Optimizing Cyclic Join View Maintenance over Distributed Data Sources
IEEE Transactions on Knowledge and Data Engineering
Distributed and Parallel Databases
Validated cost models for sensor network queries
Proceedings of the Sixth International Workshop on Data Management for Sensor Networks
Black-box determination of cost models' parameters for federated stream-processing systems
Proceedings of the 15th Symposium on International Database Engineering & Applications
KNN based evolutionary techniques for updating query cost models
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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A major challenge for global query optimization in a multidatabase system (MDBS) is lack of local cost information at the global level due to local autonomy. A number of methods to derive local cost models have been suggested in the literature recently. However, these methods are only suitable for a static multidatabase environment.In this paper, we propose a new multi-states query sampling method to develop local cost models for a dynamic environment. The system contention level at a dynamic local site is divided into a number of discrete contention states based on the costs of a probing query.To determine an appropriate set of contention states for a dynamic environment, two algorithms based on iterative uniform partition and data clustering, respectively, are introduced. A qualitative variable is used to indicate the contention states for the dynamic environment.The techniques from our previous (static) query sampling method, including query sampling, automatic variable selection, regression analysis, and model validation, are extended so as to develop a cost model incorporating the qualitative variable for a dynamic environment. Experimental results demonstrate that this new multi-states query sampling method is quite promising in developing useful cost models for a dynamic multidatabase environment.