Dynamic Temporal Interpretation Contexts for Temporal Abstraction

The temporal-abstraction task is the task of abstracting higher-level concepts from time-stamped data in a context-sensitive manner. We have developed and implemented a formal knowledge-based framework for decomposing and solving that task that supports acquisition, maintenance, reuse, and sharing of temporal-abstraction knowledge. We present the logical model underlying the representation and runtime formation of interpretation contexts. Interpretation contexts are relevant for abstraction of time-oriented data and are induced by input data, concluded abstractions, external events, goals of the temporal-abstraction process, and certain combinations of interpretation contexts. Knowledge about interpretation contexts is represented as a context ontology and as a dynamic induction relation over interpretation contexts and other proposition types. Induced interpretation contexts are either basic, composite, generalized, or nonconvex. We discuss the advantages of separating explicitly interpretation-context propositions from the propositions inducing them and from the abstractions created within them.

Authors

• Yuval Shahar

Keywords

• Medical treatment
• Computerized monitoring
• Protocols
• Biomedical informatics
• Context modeling
• Runtime
• Ontologies
• Medical diagnostic imaging
• Drugs
• Patient monitoring
• Temporal Abstraction
• Non-convex
• External Events
• Data Abstraction
• Time-stamped Data
• Time Interval
• Interpretation Of Data
• Distancing Measures
• Clinical Protocol
• Domain Experts
• Myelosuppression
• Time Stamp
• Intervals For Parameters
• Blood Glucose Values
• Temporal Constraints
• Forms Of Reasoning
• Preprandial