Principles of artificial intelligence
Principles of artificial intelligence
Randomized algorithms for optimizing large join queries
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
On the propagation of errors in the size of join results
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
R: an overview of the architecture
Readings in database systems (2nd ed.)
An overview of query optimization in relational systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Counting, enumerating, and sampling of execution plans in a cost-based query optimizer
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Iterative dynamic programming: a new class of query optimization algorithms
ACM Transactions on Database Systems (TODS)
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Distributed Query Evaluation in Local Area Networks
Proceedings of the First International Conference on Data Engineering
A Blackboard Architecture for Query Optimization in Object Bases
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Heuristic and randomized optimization for the join ordering problem
The VLDB Journal — The International Journal on Very Large Data Bases
Estimating compilation time of a query optimizer
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
The optimization of query processing on distributed database systems
The optimization of query processing on distributed database systems
Query Processing in Distributed Database System
IEEE Transactions on Software Engineering
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A new approach to query optimization, truly adaptive optimization (TAO), is presented. TAO is a general optimization strategy and is composed of three elements: 1. a fast solution space search algorithm, derived from A*, which uses an informed heuristic lookahead; 2. a relaxation technique which allows to specify a tolerance on the quality of the resulting query execution plan; 3. a paradigm to prove the suboptimality of search subspaces. Non-procedural pruning rules can be used to describe specific problem knowledge, and can be easily added to the optimizer, as the specific problem becomes better understood. The main contribution over previous research is the use of relaxation techniques and that TAO provides a unifying framework for query optimization problems, which models a complexity continuum going from fast heuristic searches to exponential optimal searches while guaranteeing a selected plan quality. In addition, problem knowledge can be exploited to speed the search up. As a preliminary example, the method is applied to query optimization for databases distributed over a broadcast network. Simulation results are reported.