DYNAMIC TEMPORAL INTERPRETATION CONTEXTS FOR TEMPORAL ABSTRACTION

Temporal abstraction is the task of abstracting higher-level concepts from time-stamped data in a context-sensitive manner. We have develope d and implemented a formal knowledge-based framework for decomposing a nd solving that task that supports acquisition, maintenance, reuse, an d sharing of temporal-abstraction knowledge. We present the logical mo del underlying the representation and runtime formation of interpretat ion contexts. Interpretation contexts are relevant for abstraction of time-oriented data and are induced by input data, concluded abstractio ns, external events, goals of the temporal-abstraction process, and ce rtain combinations of interpretation contexts. Knowledge about interpr etation 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, g eneralized, or nonconvex. We provide two examples of applying our mode l using an implemented system; one in the domain of clinical medicine (monitoring of diabetes patients) and one in the domain of traffic eng ineering (evaluation of traffic-control actions). We discuss several d istinct advantages to the explicit separation of interpretation-contex t propositions from the propositions inducing them and from the abstra ctions created within them.