The architecture of an active database management system
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
On rules, procedure, caching and views in data base systems
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
K: a high-level knowledge base programming language for advanced database applications
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Information Resources Management in Heterogeneous, Distributed Environments: A Metadatabase Approach
IEEE Transactions on Software Engineering
The metadatabase project at Rensselaer
ACM SIGMOD Record
Metadata modeling and management
Metadata modeling and management
The model-assisted global query system
The model-assisted global query system
Adaptiveness in information systems integration
Adaptiveness in information systems integration
Distributed Rule Processing in Active Databases
Proceedings of the Eighth International Conference on Data Engineering
Software Agent-Oriented Frameworks for Global Query Processing
Journal of Intelligent Information Systems
An Event Driven Software Architecture for Enterprise-Wide Data Source Integration
ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
Information Technology and Management
Enterprise Information Systems
A Metadatabase-supported shell for distributed processing and systems integration
Knowledge-Based Systems
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In some environments, it is more difficult for distributed systems to cooperate. In fact, some distributed systems are highly heterogeneous and might not readily cooperate. In order to alleviate these problems, we have developed an environment that preserves the autonomy of the local systems, while enabling distributed processing. This is achieved by 1) modeling the different application systems into a central knowledge base (called a Metadatabase), 2) providing each application system with a local knowledge processor, and 3) distributing the knowledge within these local shells. This paper is concerned with describing the knowledge decomposition process used for its distribution. The decomposition process is used to minimize the needed cooperation among the local knowledge processors, and is accomplished by "serializing" the rule execution process. A rule is decomposed into a ordered set of subrules, each of which is executed in sequence and located in a specific local knowledge processor. The goals of the decomposition algorithm are to minimize the number of subrules produced, hence reducing the time spent in communication, and to assure that the sequential execution of the subrules is "equivalent" to the execution of the original rule.