C. Galindo-Legaria, J. Pellenkoft, M.L. Kersten. Cost distributions of search spaces in query optimization. Report CS-R9432 CWI Amsterdam, July 1994.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....hierarchical level different cost models which define methods of query evaluation. The next level of the architecture description is the level 3; it comprises search strategies. In the literature several strategies are proposed including variations of enumerative search [23] and randomized search [14, 7, 10]. Our approach offers means for representing the whole class of well known strategies as a special case of a data driven strategy for state space search as formulated in artificial intelligence. A search strategy can be very briefly described in general as follows: Search from the state S i 1. ....

....or the way of selecting the succcessor to be processed next. Particular strategies are represented by modules which are coupled with the module that represents the generic strategy. Those strategies which are based on different principles, such as the so called transformation free strategy [10] are represented by a special module. For illustration we show a representation of the generic strategy from which e.g. simulated annealing can be derived. searchstrategy ismod generic strategy strategy( StopCond ] SearchedSpace, MinState) call(StopCond) stop condition satisfied ....

C. Galindo-Legaria, J. Pellenkoft, M.L. Kersten. Cost distributions of search spaces in query optimization. Report CS-R9432 CWI Amsterdam, July 1994.

....hierarchical level different cost models which define methods of query evaluation. The next level of the architecture description is the level 3; it comprises search strategies. In the literature several strategies are proposed including variations of enumerative search [23] and randomized search [14, 7, 10]. Our approach offers means for representing the whole class of well known strategies as a special case of a data driven strategy for state space search as formulated in artificial intelligence. A search strategy can be very briefly described in general as follows: Search from the state S i 1. ....

....or the way of selecting the succcessor to be processed next. Particular strategies are represented by modules which are coupled with the module that represents the generic strategy. Those strategies which are based on different principles, such as the so called transformation free strategy [10] are represented by a special module. For illustration we show in Figure 2 a representation of the generic strategy from which e.g. simulated annealing can be derived. When goal strategy(Optimiz Par, Searched Space, Best Space) is to be solved in the module search strategy the specific strategy ....

C. Galindo-Legaria, J. Pellenkoft, M.L. Kersten. Cost distributions of search spaces in query optimization. Report CS-R9432 CWI Amsterdam, July 1994.

....to a plan that is twice as expensive as the best found by any method. To analyze the results we computed the average and standard deviation of the solutions S II n , S SA n and S TF n , for 1 n 10; 000. The result of this analysis, for a query of 12 relations, is shown in figure 8 and 9. In [GLPK94a] more details on the performed experiments can be found. To get a more readable graph, figures 8 and 9 are limited to 4,000 plans. The graph is typical of the results we obtained on all queries and catalogs examined. The average of the solutions found after 10,000 plans by each algorithm were ....

C. Galindo-Legaria, A. Pellenkoft, and M. Kersten. Cost distributions of search spaces in query optimization. Technical Report CS-R9432, CWI, 1994.

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