Model-based visualization of temporal abstractions

Yuval Shahar, Cleve Cheng

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


We describe a new conceptual methodology and related computational architecture called Knowledge-based Navigation of Abstractions for Visualization and 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 raw data and the multiple levels of higher level, interval-based concepts that can be abstracted from these data. The KNAVE domain-independent exploration operators are based on the relations defined in the knowledge-based temporal-abstraction problem-solving method, which is used to abstract the data, and thus can directly use the domain-specific knowledge base on which that method relies. Thus, the domain-specific semantics are driving the domain-independent visualization and exploration processes, and the data are viewed through a filter of domain-specific knowledge. By accessing the domain-specific temporal-abstraction knowledge base and the domain-specific time-oriented database, the 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 exploration) the same domain model acquired for abstraction purposes. We focus here on the methodology, but also describe a preliminary evaluation of the KNAVE prototype in a medical domain. Our experiment incorporated seven users, a large medical patient 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 interactive data mining of time-oriented data.

Original languageEnglish
Pages (from-to)279-306
Number of pages28
JournalComputational Intelligence
Issue number2
StatePublished - 1 Jan 2000
Externally publishedYes

ASJC Scopus subject areas

  • Computational Mathematics
  • Artificial Intelligence


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