Learn More
References [1] Leonidas Fegaras. A new heuristic for optimizing large queries. [2] Toshihide Ibaraki and Tiko Kameda. On the optimal nesting order for computing n-relational joins. Optimizing large join queries using a graph-based approach. [5] Guido Moerkotte and Thomas Neumann. Analysis of two existing and one new dynamic programming algorithm for the(More)
Query optimizers that explore a search space exhaustively using transformation rules usually apply all possible rules on each alternative , and stop when no new information is produced. A memoizing structure was proposed in McK93] to improve the re-use of common subexpression, thus improving the eeciency of the search considerably. However, a question that(More)
We study the effectiveness of probabilistic selection of join-query evaluation plans, t&h-out reliance on tree transformation rules. Instead , each candidate plan is chosen uniformly at random from the space of valid evaluation orders. This leads to a transformation-free strategy where a sequence of random plans is generated and the plans are compared on(More)
In this paper we study the space of operator trees that can be used to answer a join query, with the goal of generating elements form this space at random. We solve the problem for queries with acyclic query graphs. We rst count, in O(n 3) time, the exact number of trees that can be used to evaluate a given query on n relations. The intermediate results of(More)
Transformation-based optimizers that explore a search space exhaustively usually apply all possible transformation rules on each alternative, and stop when no new information is produced. In general, diierent sequences of transformation rules may end up deriving the same element. The optimizer must detect and discard these duplicate elements generated by(More)
Transformation-based optimizers that explore a search space exhaustively usually apply all possible transformation rules on each alternative, and stop when no new information is produced. In general, diierent sequences of transformations may end up deriving the same element. The optimizer must detect and discard these duplicate elements. In this paper we(More)
Query optimization algorithms explore a large space of query execution plans looking for an optimal solution. The predominant algorithms move around the search space in either a deterministic or probabilistic way. The performance of probabilistic optimization algorithms is strongly innuenced by the cost distribution over the search space, the connectivity(More)
Large-scale query optimization is, besides its practical relevance, a hard test case for optimization techniques. Since exact methods cannot be applied due to the combinatorial explosion of the search space, heuristics and probabilistic strategies have been deployed for more than a decade. However, the results achieved are subject to discussion as several(More)