יובל שחר

Temporal abstraction 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 provide two examples of applying our model using an implemented system; one in the domain of clinical medicine (monitoring of diabetes patients) and one in the domain of traffic engineering (evaluation of traffic-control actions). We discuss several distinct advantages to the explicit separation of interpretation-context propositions from the propositions inducing them and from the abstractions created within them.