A Framework for Knowledge-Based Temporal Abstraction

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events, and contexts. A formal specification of a domainÕs temporal-abstraction knowledge supports acquisition, maintenance, reuse, and sharing of that knowledge. The knowledge-based temporal-abstraction method decomposes the temporal-abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal-abstraction mechanisms. The temporal-abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal-semantic (logical), and temporal-dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal-abstraction system is developed. The knowledge-based temporal-abstraction method has been implemented in the RƒSUMƒ system, and has been evaluated in several clinical domains (protocol-based care, monitoring of childrenÕs growth, and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.