Arjan Pellenkoft

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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 efficiency of the search considerably. However, a question(More)
Uniform sampling of join orders is known to be a competitive alternative to transformation-based optimization techniques. However, uniformity of the sampling process is difficult to establish and only for a restricted class of join queries techniques are known. In this paper, we investigate non-uniform sampling devising a simple yet powerful algorithm that(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)
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)
We study the effectiveness of probabilistic selection of join-query evaluation plans, t&hout 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)
We study the eeectiveness of probabilistic selection of join-query evaluation plans without 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 their(More)
Join-ordering is known to be NP-complete and therefore a variety of heuristics have been devised to tackle large queries which are considered computational intractable otherwise. However, practitioners often point out that typical problem instances are not di cult to optimize at all. In this paper we address that seeming discrepancy. We present a(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)