Magic sets and other strange ways to implement logic programs (extended abstract)
PODS '86 Proceedings of the fifth ACM SIGACT-SIGMOD symposium on Principles of database systems
An amateur's introduction to recursive query processing strategies
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Database compression with data mining methods
Information organization and databases
| Hi-index | 0.00 |
Deductive databases have the ability to deduce new facts from a set of facts using a set of rules. They are also useful in the integration of artificial intelligence and database. However, when recursive rules are involved, the amount of deduced facts can become too large to be practically stored, viewed or analyzed. This seriously hinders the usefulness of deductive databases. In order to overcome this problem, we propose four methods to discover characteristic rules from large amount of deduction results without actually having to store all the deduction results. This paper presents the first step in the application of knowledge discovery techniques to deductive databases with large deduction results.