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Semantic Query Optimization for Query Plans of Heterogeneous Multidatabase Systems
November/December 2000 (vol. 12 no. 6)
pp. 959-978

Abstract—New applications of information systems, such as electronic commerce and healthcare information systems, need to integrate a large number of heterogeneous databases over computer networks. Answering a query in these applications usually involves selecting relevant information sources and generating a query plan to combine the data automatically. As significant progress has been made in source selection and plan generation, the critical issue has been shifting to query optimization. This paper presents a semantic query optimization (SQO) approach to optimizing query plans of heterogeneous multidatabase systems. This approach provides global optimization for query plans as well as local optimization for subqueries that retrieve data from individual database sources. An important feature of our local optimization algorithm is that we prove necessary and sufficient conditions to eliminate an unnecessary join in a conjunctive query of arbitrary join topology. This feature allows our optimizer to utilize more expressive relational rules to provide a wider range of possible optimizations than previous work in SQO. The local optimization algorithm also features a new data structure called AND-OR implication graphs to facilitate the search for optimal queries. These features allow the global optimization to effectively use semantic knowledge to reduce data transmission cost. We have implemented this approach into the pesto query plan optimizer as a part of the sims information mediator. Experimental results demonstrate that pesto can provide significant savings in query execution cost over query plan execution without optimization.

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Index Terms:
Semantic query optimization, heterogeneous multidatabase systems, relational rules, joins, information mediators.
Citation:
Chun-Nan Hsu, Craig A. Knoblock, "Semantic Query Optimization for Query Plans of Heterogeneous Multidatabase Systems," IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 6, pp. 959-978, Nov.-Dec. 2000, doi:10.1109/69.895804
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