A new approach is presented that uses knowledge induction and type inference to provide intensional answers. Machine learning techniques are used to analyze database contents and to induce a set of if-then rules. Type inference which is based on forward inference and backward inference is developed that uses database type hierarchies to derive the intensional answers for a query. It is shown that more precise intensional answers can be derived by properly merging the type inference results from multiple type hierarchies. A prototype intensional query-processing system which uses the proposed approach has been implemented. Using a ship database as a testbed, the effectiveness of the use of type interference and induced rules to derive specific intensional answers is demonstrated.>

A knowledge-gased approach for query processing during network partitioning is proposed. The approach uses available domain and summary knowledge to infer inaccessible data to answer a given query. A rule induction technique is used to extract correlated knowledge between attributes from the database contents. This knowledge is represented as rules for data inference. On the basis of a set of queries, simulation is used to evaluate the effectiveness of the proposed data inference technique for improving data availability under network partitioning. Object allocation has a significant impact on data availability. Allocating objects that increase remote redundancy and reduce local redundancy increases data Availability during network partitioning. A prototype distributed database system that uses the proposed inference technique with correlated knowledge from a ship database has been implemented. Experience indicates that the proposed inference technique can significantly improve the availability of a distributed database during network partitioning.>

An expert system for network management is designed and prototyped to do network troubleshooting automatically. The expert system employs management information provided by a monitoring mechanism of the network. The whole spectrum of the fault management information is analyzed. The management knowledge derived is categorized into five types: the physical property, the experience in the past, the heuristic rule of thumb, the predictable problems, and the deep knowledge. These knowledge types and related rules are divided into groups to improve reasoning speed. The expert system is composed of a problem manager, a problem analyzer, and many problem solvers. A prototyped expert system, using simulated faults, shows that the expert-system-based fault management system can automatically diagnose problems and take corrective actions.>