Federated database systems for managing distributed, heterogeneous, and autonomous databases
ACM Computing Surveys (CSUR) - Special issue on heterogeneous databases
Interoperability of multiple autonomous databases
ACM Computing Surveys (CSUR) - Special issue on heterogeneous databases
On global multidatabase query optimization
ACM SIGMOD Record
The CORDS multidatabase project
IBM Systems Journal
Reducing multidatabase query response time by tree balancing
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Query caching and optimization in distributed mediator systems
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Multidatabase Query Optimization
Distributed and Parallel Databases
Solving Local Cost Estimation Problem for Global Query Optimization in Multidatabase Systems
Distributed and Parallel Databases
Stochastic processes
A fuzzy query optimization approach for multidatabase systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Building regression cost models for multidatabase systems
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Query Optimization in Multidatabase Systems Considering Schema Conflicts
IEEE Transactions on Knowledge and Data Engineering
A Query Sampling Method of Estimating Local Cost Parameters in a Multidatabase System
Proceedings of the Tenth International Conference on Data Engineering
Cost Models DO Matter: Providing Cost Information for Diverse Data Sources in a Federated System
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Query Optimization in a Heterogeneous DBMS
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Calibrating the Query Optimizer Cost Model of IRO-DB, an Object-Oriented Federated Database System
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Leveraging Mediator Cost Models with Heterogeneous Data Sources
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Developing Cost Models with Qualitative Variables for Dynamic Multidatabase Environments
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Design of an embedded cost model for mobile queries
UbiMob '04 Proceedings of the 1st French-speaking conference on Mobility and ubiquity computing
A monitoring service for large-scale dynamic query optimisation in a grid environment
International Journal of Web and Grid Services
Evolution of Query Optimization Methods: From Centralized Database Systems to Data Grid Systems
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
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Accurate query cost estimation is crucial to query optimization in a multidatabase system. Several estimation techniques for a static environment have been suggested in the literature. To develop a cost model for a dynamic environment, we recently introduced a multistate query-sampling method. It has been shown that this technique is promising in estimating the cost of a query run in any given contention state for a dynamic environment. In this paper, we study a new problem on how to estimate the cost of a large query that may experience multiple contention states. Following the discussion of limitations for two simple approaches, i.e., single state analysis and average cost analysis, we propose two novel techniques to tackle this challenge. The first one, called fractional analysis, is suitable for a gradually and smoothly changing environment, while the second one, called the probabilistic approach, is developed for a rapidly and randomly changing environment. The former estimates a query cost by analyzing its fractions, and the latter estimates a query cost based on Markov chain theory. The related issues including cost formula development, error analysis, and comparison among different approaches are discussed. Experiments demonstrate that the proposed techniques are quite promising in solving the new problem.