| Hongjun Lu, Kian-Lee Tan, and Son Dao. The fittest survives: An adaptive approach to query optimization. In The VLDB Journal, pages 251--262, 1995. |
....join expression q = R 1 . R 2 . R 3 . In order to explain our approach of regrouping the join exchange rules, the following conventions must be introduced. A join will always depend on the incoming 1 We join the common hypotheses to exclude cartesien products from the parallel search space [20, 40, 10]. 2 The processing tree is a representation of the relational algebra [7] 11 J J 1 2 R R R 1 2 3 Level 0: Level 1: Level 2: Figure 2: Sample processing tree relations that are subject to this join. This way, a parent child relationship is established between this join and the base ....
....search strategies are implementation dependent and must be tuned to improve performance and output quality. The following Tables 1 for the RII and 2 for the TPO shows the parameter settings by previous work, which deal with sequential (Ioannidis et al. 36] Swami et al. 35] and Lu et al. [40]) and parallel (Lanzelotte et al. 16, 20] and Spilipoulou et al. 10] optimization. Let n be the number of relations in the query. The double question mark : indicates the setting, not mentioned in previous work. For the RII two parameters must be fixed : the number of II runs (runs) and how ....
H. Lu, K.-L. Tan, and S. Dao. The fittest survives: An adaptive Approach to Query Optimization. In Proceedings of the International Conference on Very Large Databases, pages 251--262, Zurich, Switzerland, September 1995.
....sorting [SSM96] ffl Semantics and Cost Estimation [NCN97] 7. 5 Plan Languages, Partial Evaluation and Dynamic Optimization ffl Exodus Volcano [CDG 90, GM93] ffl OPA [DGK 91, Gra95] ffl Opt [KD] ffl Graefe Cole Ward [GW89, CG94] ffl Ioannidis [INSS92] ffl Adaptive Optimization [LTD96] 132 ffl Antoshenkov [Ant96, AZ96] ffl Query Scrambling [AFTU96] 133 8 Future Work The proposed thesis will have the following structure: ffl Section 1: Introduction (from Section 1 of proposal) ffl Section 2: Motivation (from Section 2 of proposal) ffl Section 3: KOLA (from Section 3 of ....
Hongjun Lu, Kian-Lee Tan, and Son Dao. Fittest survives: An adaptive approach to query optimization. In Proceedings of the 22nd VLDB Conference, pages 251--262, Bombay, India, September 1996.
....which are unbound at compile time must be defined or estimated no later than start up time. Adaptive Optimization tries to improve the execution plan for a canned query after each run. With a query feedback mechanism, 2] approximates the attribute value distribution with a curve fitting function. [17] divides and orders the plan search space into subspaces. A plan is produced based on the current system parameters and the performance of the past generated plans for the same query such that an incremental optimization can be done. Recently, there is some related work in this area ( 1] 16] ....
H. Lu, K. Tan, and S. Dao. The Fittest Survives: An Adaptive approach to Query Optimization. In Proceedings of VLDB, 1995.
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Hongjun Lu, Kian-Lee Tan, and Son Dao. The fittest survives: An adaptive approach to query optimization. In The VLDB Journal, pages 251--262, 1995.
No context found.
H. Lu, K.-L. Tan, and S. Dao. The Fittest Survives: An Adaptive Approach to Query Optimization. In Proceedings of 21st VLDB Conference, pp. 251--262, 1995.
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