A new approach to the maximum flow problem
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
Towards an open architecture for LDL
VLDB '89 Proceedings of the 15th international conference on Very large data bases
Predicate migration: optimizing queries with expensive predicates
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Approximating clique and biclique problems
Journal of Algorithms
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Processing Queries with Expensive Functions and Large Objects in Distributed Mediator Systems
Proceedings of the 17th International Conference on Data Engineering
Query Optimization in the Presence of Foreign Functions
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
A new strategy for querying priced information
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Querying priced information in databases: The conjunctive case
ACM Transactions on Algorithms (TALG)
A randomized competitive algorithm for evaluating priced AND/OR trees
Theoretical Computer Science
An optimal algorithm for querying priced information: monotone boolean functions and game trees
ESA'05 Proceedings of the 13th annual European conference on Algorithms
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Query optimization problems for expensive predicates have received much attention in the database community. In these situations, the output to the database query is a set of tuples that obey certain conditions, where the conditions may be expensive to evaluate computationally. In the simplest case when the query looks for the set of tuples that simultaneously satisfy two expensive conditions on the tuples and these can be checked in two different distributed processors, the problem reduces to one of ordering the condition evaluations at each processor to minimize the time to output all the tuples that are answers to the query. We improve upon a previously known deterministic 3-approximation for this problem: In the case when the times to evaluate all conditions at bothp rocessors are identical, we give a 2-approximation; In the case of non-uniform evaluation times, we present a 8/3-approximation that uses randomization. While it was known earlier that no deterministic algorithm (even with exponential running time) can achieve a performance ratio better than 2, we show a corresponding lower bound of 3/2 for any randomized algorithm.