ADAPTIVE QUERY OPTIMIZATION IN DYNAMIC DATABASES
| Autor: | MIN J. YU, P. C-Y. SHEU |
|---|---|
| Rok vydání: | 1998 |
| Předmět: |
Push–relabel maximum flow algorithm
Meta-optimization Theoretical computer science Database Adaptive algorithm Computer science Population-based incremental learning computer.software_genre Query optimization Artificial Intelligence Algorithm design Suurballe's algorithm Data mining computer FSA-Red Algorithm |
| Zdroj: | International Journal on Artificial Intelligence Tools. :1-30 |
| ISSN: | 1793-6349 0218-2130 |
| DOI: | 10.1142/s0218213098000020 |
| Popis: | Query optimization of database management systems is aimed at finding an optimal access path for a query to minimize the evaluation cost. This research addresses the problem of query optimization for databases in which objects frequently change their values. A greedy, adaptive query optimization algorithm is proposed to evaluate relational queries and queries containing complex objects. Rather than constructing a full plan for an access path and executing it, the algorithm constructs a partial plan, executes it, updates the statistics, and constructs a new partial plan. Since a partial plan is constructed based on the latest statistics, the algorithm is adaptive to data modifications and errors from the statistics. It is proved that the algorithm can produce an optimal solution for a class of queries. Furthermore, experiments show that the overhead associated with the algorithm is negligible and the algorithm is efficient for other cases. An adaptive query optimization algorithm for distributed environments is also proposed. The algorithm extends the SDD-1 algorithm to local area networks by considering local processing cost as well as communication cost. Whereas the SDD-1 algorithm only uses semi-joins to reduce communication cost, the algorithm reduces it with joins as well. It is proved that the adaptive algorithm is more efficient than the SDD-1 algorithm. |
| Databáze: | OpenAIRE |
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