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
Federated database systems for managing distributed, heterogeneous, and autonomous databases
ACM Computing Surveys (CSUR) - Special issue on heterogeneous databases
Information gathering in the World-Wide Web: the W3QL query language and the W3QS system
ACM Transactions on Database Systems (TODS)
A Generic Query-Translation Framework for a Mediator Architecture
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
A Suitable Algorithm for Computing Partial Transitive Closures in Databases
Proceedings of the Sixth International Conference on Data Engineering
The Efficient Computation of Strong Partial Transitive-Closures
Proceedings of the Ninth International Conference on Data Engineering
Optimizing Queries Across Diverse Data Sources
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Monitoring the progress of anytime problem-solving
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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Evaluation of recursive queries and computing transitive closures require multiple accesses to the involved relations. In a federated database this leads to multiple accesses to the participants of the federation. Since the components are not uniform in terms of computation power, reliability, and communication delays, it might be desirable to minimize the number of accesses to the individual databases, and to maximize the size of the obtained answer with respect to time.Based on this observation, we developed cooperative query planning methods, termed Deep Federated Semi-Naive (DFSN), for computing the strong partial transitive closure of a relation. We have implemented and tested these algorithms in a real database environment. The experimental results show better performance of the DFSN methods over the conservative semi-naive approaches in that they produce large answer sets in time that is considerately shorter than the time needed by the conservative approaches.