G. Graefe, The Cascades Framework for Query Optimization, Bulletin of the IEEE Technical Committee on Data Engineering, 18(3), September 1995, Pg. 19-29. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Exploiting Upper and Lower Bounds in Top-Down Query.. - Shapiro, Maier.. (2001) (Correct)
....programming techniques to greatly simplify the task of constructing or extending an optimizer, while maintaining efficiency and making search strategies even more flexible. Examples of this third generation of optimizers are the OPT system from Wisconsin [KaD96] and Graefes Cascades system [Gra95]. 1) For i = 1, N (2) For each set S containing exactly i of the N tables (3a) Generate all appropriate plans for joining the tables in S, 3b) considering only plans with optimal inputs, and (3c) retaining the optimal generated plan for each set of interesting physical properties. Figure ....
G. Graefe, The Cascades Framework for Query Optimization, Bulletin of the IEEE Technical Committee on Data Engineering, 18(3), September 1995, Pg. 19-29.
The Use of Randomized Search Strategies for Complex Parallel.. - Kosch (Correct)
.... programming and randomized search Finally it should be remarked that the dynamic programming optimization can be done also in a top down manner (we start with complete trees and then try to arrange the subtrees) The latter method became recently popular as we generate very fast complete trees [32, 33]. If we apply for parallel query optimization bottom up and top down optimization suffer from the same optimization time explosion [21] 3.3 Randomized search For managing inter operator parallelism in complex, queries, randomized search strategies have been suggested as one alternative to ....
Goetz Graefe. The Cascades Framework for Query Optimizatiom. Bulletin of the IEEE Technical Committee on Data Engineering, 18(3):19--29, September 1995.
Exploiting Upper and Lower Bounds in Top-Down Query.. - Shapiro, Maier.. (2001) (Correct)
....programming techniques to greatly simplify the task of constructing or extending an optimizer, while maintaining efficiency and making search strategies even more flexible. Examples of this third generation of optimizers are the OPT system from Wisconsin [KaD96] and Graefe s Cascades system [Gra95]. OPT compared the performance of top down and bottom up optimizers. But it used Volcano s O(3 N ) generation strategy for the top down case, which yielded poor performance in OPT benchmarks. Cascades was developed to demonstrate both the extensibility of the object oriented approach and the ....
G. Graefe, The Cascades Framework for Query Optimization, Bulletin of the IEEE Technical Committee on Data Engineering, 18(3), September 1995, Pg. 19-29.
Nested Transactions with Integrity Constraints - Doucet, Gançarski.. (Correct)
....to allow modeling complex long duration transactions and distributed applications in cooperative environments. In order to fulfill this need for more flexibility, other transaction models have arisen, such as nested transactions [Mos85] multi level transactions [Wei86] Sagas [GS87] ConTracts [RW91], transactional activity model [DHL91] among others. A detailed revision about advanced models of transactions can be seen in [GR93] However, none of the extended models defined up to now has turned out to be general enough to accommodate all types of applications. ACTA [CR90,CR94] is a ....
A. Reuter and H. Wachter. The ConTract Model. Bulletin of the IEEE Technical Committee on Data Engineering, 14(1):39--44, 1991.
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