ACM Computing Surveys (CSUR)
On global multidatabase query optimization
ACM SIGMOD Record
Query Optimization in a Heterogeneous DBMS
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Query optimization in multidatabase systems
CASCON '92 Proceedings of the 1992 conference of the Centre for Advanced Studies on Collaborative research - Volume 2
An integrated method for estimating selectivities in a multidatabase system
CASCON '93 Proceedings of the 1993 conference of the Centre for Advanced Studies on Collaborative research: distributed computing - Volume 2
The CORDS multidatabase project
IBM Systems Journal
CORDS multidatabase project: research and prototype overview
CASCON '93 Proceedings of the 1993 conference of the Centre for Advanced Studies on Collaborative research: distributed computing - Volume 2
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One of the challenges for global query optimization in a multidatabase system (MDBS) is that some local optimization information may not be accurately known at the global level because of local autonomy. In this paper, we introduce a fuzzy query optimization approach that is based on fuzzy set theory, introduced by Zadeh in the 1960s, to tackle the challenge. We describe the problem of fuzzy query optimization and compare the fuzzy optimization approach with the traditional (crisp) optimization approach. We show that the fuzzy approach has a better chance to find a good execution strategy for a query than the crisp approach in an MDBS environment, but the complexity of the former may grow exponentially as the complexity of the later. To reduce the complexity, we use a weighted k-approximate fuzzy value to approximate every fuzzy value during fuzzy query optimization. It is proven that the improved fuzzy approach has the same order of complexity as the crisp approach.