Expert systems for real time process monitoring need to respond to asynchronous events and reason about temporal properties of the process. This requires the ability to perform data input, event handling and temporal asynchronous reasoning during the inference cycle. Conventional expert systems do not possess these abilities. We describe a knowledge representation formalism which captures dynamic properties like external events, timing constraints and data trends in addition to diagnostic knowledge. This representation lends itself to verification of the knowledge base. We further show the implementation of a multiple rule firing asynchronous inference engine using this model.>

The transformation of existing local databases to meet diverse application needs at the global level is performed through a four-layered procedure that stresses total schema integration and virtual integration of local databases. The proposed methodology covers both schema integration and database integration, and uses a four-layered schema architecture (local schemata, local object schemata, global schema, and global view schemata) with each layer presenting an integrated view of the concepts that characterize the layer below. Mechanisms for accomplishing this objective are presented in theoretical terms, along with a running example. Object equivalence classes, property equivalence classes, and other related concepts are discussed in the context of logical integration of heterogeneous schemata, while object instance equivalence classes and property instance equivalence classes, and other related concepts are discussed for data integration purposes. The proposed methodology resolves naming conflicts, scaling conflicts, type conflicts, and level of abstraction, and other types of conflicts during schema integration, and data inconsistencies during data integration.>