יובל שחר

We describe a domain-independent framework (KNAVE) specific to the task of interpretation, summerization, visualization, explanation, and interactive navigation in a context-sensitive manner through time-oriented raw data and the multiple levels of higher-level, interval-based concepts that can be abstracted from these data. The KNAVE domain-independent navigation operators access the domain-specific knowledge base, which is modeled by the formal ontology of the knowledge-based temporal-abstraction method; the method generates the temporal abstractions from the time-oriented database. Thus, domain-specific knowledge underlies the semantics of the domain-independent visualization and navigation processes. By accessing the domain-specific temporal abstraction knowledge base and the domain-specific time-oriented database, KNAVE modules enable users to query for domain-specific temporal abstractions and to change the focus of the visualization, thus reusing for a different task (visualization and navigation) the domain model that has been acquired from the domain experts for the purpose of temporal abstraction. Initial evaluation of the KNAVE prototype has been encouraging. The KNAVE methodology has potentially broad implications for tasks such as planning, monitoring, explanation, and data mining.