Communications of the ACM
An amateur's introduction to recursive query processing strategies
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
SOAR: an architecture for general intelligence
Artificial Intelligence
Principles of artificial intelligence
Principles of artificial intelligence
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Controlling backward inference
Artificial Intelligence
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Recursive query processing: the power of logic
Theoretical Computer Science
An introduction to database systems
An introduction to database systems
On the sample complexity of finding good search strategies
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Finding optimal derivation in redundant knowledge bases
Artificial Intelligence
Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment
Query size estimation by adaptive sampling (extended abstract)
PODS '90 Proceedings of the ninth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Optimizing existential datalog queries
Proceedings of the seventh ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Procedural and declarative database update languages
Proceedings of the seventh ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Index selection in a self-adaptive data base management system
SIGMOD '76 Proceedings of the 1976 ACM SIGMOD international conference on Management of data
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Solving Time-Dependent Planning Problems
Solving Time-Dependent Planning Problems
| Hi-index | 0.00 |
A query processor QP uses the rules in a rule base to reduce a given query to a series of attempted retrievals from a database of facts. The Qp's expected cost is the average time it requires to find an answer, averaged over its anticipated set of queries. This cost depends on Qp's strategy, which specifies the order in which it considers the possible rules and retrievals. This paper provides two related learning algorithms, PIB and PAO, for improving the QP's strategy, i.e., for producing new strategies with lower expected costs. Each algorithm first monitors the Qp's operations over a set of queries, observing how often each path of rules leads to a sufficient set of successful retrievals, and then uses these statistics to suggest a new strategy. PIB hill-climbs to strategies that are, with high probability, successively better; and PAO produces a new strategy that probably is approximately optimal. We describe how to implement both learning systems unobtrusively, discuss their inherent time and space complexities, and use methods from mathematical statistics to prove their correctness. We also discuss additional applications of these approaches to several other database tasks.