Optimization of distributed tree queries
Journal of Computer and System Sciences
ACM Computing Surveys (CSUR)
Query optimization by simulated annealing
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Simulated Annealing, Random Search, MultiStart or SAD?
Systems & Control Letters
ACM Transactions on Database Systems (TODS)
Optimization of large join queries
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Optimizing Join Queries in Distributed Databases
IEEE Transactions on Software Engineering
Randomized algorithms for optimizing large join queries
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Using Semi-Joins to Solve Relational Queries
Journal of the ACM (JACM)
R* Optimizer Validation and Performance Evaluation for Distributed Queries
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
On the Effectiveness of Optimization Search Strategies for Parallel Execution Spaces
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Processing Distributed Mobile Queries with Interleaved Remote Mobile Joins
IEEE Transactions on Computers
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
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In relational distributed databases a query cost consists of a local cost and a transmission cost. Query optimization is a combinatorial optimization problem. As the query size grows, the optimization methods based on exhaustive search become too expensive. We propose the following strategy for solving large distributed query optimization problems in relational database systems: 1) represent each query-processing schedule by a labeled directed graph, 2) reduce the number of different schedules by pruning away invalid or high-cost solutions, and 3) find a suboptimal schedule by combinatorial optimization. We investigate several combinatorial optimization techniques: random search, single start, multistart, simulated annealing, and a combination of random search and local simulated annealing. The utility of combinatorial optimization is demonstrated in the problem of finding the (sub)optimal semijoin schedule that fully reduces all relations of a tree query. The combination of random search and local simulated annealing was superior to other tested methods.