Model-based visualization of temporal abstractions

We describe a new conceptual methodology and related computational architec ture called Knowledge-based Navigation of Abstractions for Visualization an d Explanation (KNAVE). KNAVE is a domain-independent framework specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration, in a context-sensitive manner, of time-oriented ra w data and the multiple levels of higher level, interval-based concepts tha t can be abstracted from these data. The KNAVE domain-independent explorati on operators are based on the relations defined in the knowledge-based temp oral-abstraction problem-solving method, which is used to abstract the data , and thus can directly use the domain-specific knowledge base on which tha t method relies. Thus, the domain-specific semantics are driving the domain -independent visualization and exploration processes, and the data are view ed through a filter of domain-specific knowledge. By accessing the domain-s pecific temporal-abstraction knowledge base and the domain-specific time-or iented database, the KNAVE modules enable users to query for domain-specifi c temporal abstractions and to change the focus of the visualization, thus reusing for a different task (visualization and exploration) the same domai n model acquired for abstraction purposes. We focus here on the methodology , but also describe a preliminary evaluation of the KNAVE prototype in a me dical domain. Our experiment incorporated seven users, a large medical pati ent record, and three complex temporal queries, typical of guideline-based care, that the users were required to answer and/or explore. The results of the preliminary experiment have been encouraging. The new methodology has potentially broad implications for planning, monitoring, explaining, and in teractive data mining of time-oriented data.