R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In Proc. Int. Conf. on Very Large Data Bases (VLDB), pages 128--137, 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....have any performance guarantees in terms of the quality of plans generated (i.e. the plans generated by them can be arbitrarily far from the optimal one) Many of these techniques may even fail to generate a feasible plan, while the user query does have a feasible plan. The remaining solutions [32, 39, 66] use specific cost models and clever techniques that exploit them to produce optimal join orders efficiently. While these solutions are very good for the join order problem where those cost models are appropriate, they are hard to adopt in our context because of two difficulties. The first ....

R. Krishnamurthy, H. Boral, C. Zaniolo. Optimization of Non-recursive Queries. In Proc. VLDB Conference, 1986.

....is to determine the set of filter conditions that are to be evaluated using GradeSearch. The rest of the conditions will be evaluated by using Probe. In order to efficiently execute the latter step, we will exploit the known techniques in optimizing the processing of expensive filter conditions [25, 22, 23, 26, 11]. In this section, we first define a space of search minimal executions, which access as few attributes as possible using GradeSearch, and sketch the cost model and the optimization criteria. Next, we describe an optimization algorithm and explain the conditions under which it is optimal. ....

....objects in the repository, IOal Sel(a) o. Optimizing Evaluation of Residues: Given a residue (a, f) the task of determining an optimal eval uation for (a, f) maps to the well studied problem of optimizing the execution of selection conditions containing expensive predicates [25] See also [23, 26, 22, 11]. If (a, f) is a conjunction of atomic conditions a A. A a, there is an efficient algorithm w that finds the optimum probing strategy. Specifically, it can be shown [23, 26] that the order in which the atomic conditions for each object should be probed is given by the rank of each condition ....

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In Proceedings of the Twelfth International Conference on Vew Large Databases (VLDB'86), Aug. 1986.

....System R, join trees are restricted to linear trees, so that available access structures for the inner join operand can optimally be exploited. System R chooses the cheapest (in the sense of minimal total costs) linear tree that does not contain cartesian products. Subsequently, it is remarked in [KBZ86] that the restriction to linear trees may not be a good choice for parallel systems. However, the space of possible join trees is very large if restriction to linear trees is dropped [LVZ93] In [LST91, SwG88] partially heuristic algorithms are proposed that aim at limiting the time spent on ....

....costs in the parallel execution of this schedule will be low as well. Third, two phase optimization seems a reasonable way to cut down on the optimization time. Lastly, missing the very best execution plan is not a big problem as long as you can assure that you will not come up with a very bad one [KBZ86]. The first phase of the two phase optimization can easily be handled by standard query optimization. The second phase: finding a suitable parallelization for a given join tree is the subject of this paper. 1.3 Organization of paper This paper is organized as follows. Section 2 shortly introduces ....

R. Krishnamurty, H. Boral & C. Zaniolo, "Optimization of nonrecursive queries," in Proc 12th VLDB Conf, Kyoto, Japan, August 25-28, 1986, 128 137.

....while some QEPs remain incomparable and need to be temporarily maintained. Thus, not only the size of the search space but also the memory demand during optimization are substantially increased. The overhead of exhaustive exploration of the search space increases exponentially with the query size [15]. If parallelism is also considered, the space becomes even larger [16] Hence, as large join queries emerge in applications requiring the coupling of knowledge bases with database systems [15] in deductive and in object oriented databases, researchers turn towards alternatives to exhaustive ....

.... The overhead of exhaustive exploration of the search space increases exponentially with the query size [15] If parallelism is also considered, the space becomes even larger [16] Hence, as large join queries emerge in applications requiring the coupling of knowledge bases with database systems [15], in deductive and in object oriented databases, researchers turn towards alternatives to exhaustive search, such as heuristics, dynamic programming, and combinatorial optimization techniques. Dynamic programming has been studied in [4] 25] However, dynamic programming performs an almost ....

R. Krishnamurthy, H. Boral, and C. Zaniolo, "Optimization of Nonrecursive Querie," Proc. Int'l Conf. Very Large Databases, pp. 128--137, Kyoto, Japan, 1986.

....hash join) Thus, there may be many implementation functions as instances of T algOp. The second design decision deals with the compilation of query results in the translation of a TQL expression to an algebraic expression. Algebra expressions are commonly represented as processing trees (PTs) [20]. In relational systems, a processing Appears in Proc. of the Centre for Advanced Studies Conference (CASCON) pages 595 611, October 1993. 11 T object T function T rule T formula T costFunct F leafAlgOp F SelectAlgOp F JoinAlgOp T AdHoc T omOp T heurSS T algOp T context B Join ....

R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In Proc. 12th Int. Conf. on Very Large Databases, pages 128,137, 1986.

....plans with higher probability than linear trees. 5. Related Work The join ordering problem continuously received attention during the past two decades. Besides enumeration techniques for small query sizes (cf. e.g. 16, 22, 14] heuristics have been developed in order to tackle larger instances [12, 20]. However, as Steinbrunn et al. pointed out, heuristics yield only mediocre results as the queries grow in size [17] On the other hand, beginning with [10] randomized techniques have been introduced and attracted particular interest ever since. Swami and Gupta as well as Ioannidis and Kang ....

R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of Nonrecursive Queries. In Proc. of the Int'l. Conf. on Very Large Data Bases, pages 128--137, Kyoto, Japan, August 1986.

....made by concatenating the tuples having an equal TOWN value. A relational query is a combination of those basic relational operators. Different representation forms for relational queries exists (e.g. object graph, algebra expression [15] However the representation as query processing tree [16] is the most used. In this model : The leaves of a query processing tree represent the base relations that participate in the query. Intermediate nodes model operations, which receive their input 3 relations via the incoming edges and send the result though the outgoing edge to the next ....

Krishnamurty R. Boral H. and Zaniolo C. Optimization of Nonrecursive Queries. In Proceedings of the International Conference on Very Large Databases, Kyoto, Japan, August 1986.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In Proc. Int. Conf. on Very Large Data Bases (VLDB), pages 128--137, 1986.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In Proc. Int. Conf. on Very Large Data Bases (VLDB), pages 128--137, 1986.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In Proc. Int. Conf. on Very Large Data Bases (VLDB), pages 128-- 137, 1986.

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R. Krishnamurthy, H. Boral, C. Zaniolo. Optimization of Nonrecursive Queries. VLDB, 1986, pages 128--137.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In VLDB, pages 128--137, 1986.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In Proceedings of the 12th International ConferenceonVery Large Databases, pages 128137, Kyoto, 1986.

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R. Krishnamurthy, H. Boral, C. Zaniolo, Optimization of nonrecursive queries, in: W. W. Chu, G. Gardarin, S. Ohsuga, Y. Kambayashi (Eds.), Proceedings of 12th International Conference on Very Large Data Bases, August 25-28, 1986.

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Ravi Krishnamurthy, Haran Boral, and Carlo Zaniolo. Optimization of nonrecursive queries. In VLDB, 1986.

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Ravi Krishnamurthy, Haran Boral, and Carlo Zaniolo. Optimization of nonrecursive queries. In VLDB, 1986.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In Proc. Int. Conf. on Very Large Data Bases (VLDB), pages 128--137, 1986.

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Ravi Krishnamurthy, Haran Boral, and Carlo Zaniolo. Optimization of nonrecursive queries. In Wesley W. Chu, Georges Gardarin, Setsuo Ohsuga, and Yahiko Kambayashi, editors, VLDB'86 Twelfth International Conference on Very Large Data Bases, August 25-28, 1986.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In Proc. Int. Conf. on Very Large Data Bases (VLDB), pages 128--137, 1986.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In VLDB, 1986.

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R. Krishnamurthy, H. Boral, C. Zaniolo. Optimization of Nonrecursive Queries. VLDB, 1986, pages 128--137.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In VLDB, pages 128--137, 1986.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In VLDB, 1986.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of nonrecursive queries. In VLDB, pages 128--137, 1986.

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R. Krishnamurthy, H. Boral, and C. Zaniolo. Optimization of Nonrecursive Queries. Proceedings of the 12th International Conference on Very Large Data Bases, pages 128-- 137, August 1986.

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