Fault tolerant distributed database system via data inference

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