Model-based visualization of temporal abstractions
Model-based visualization of temporal abstractions
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DOI：
10.1111/0824-7935.00114
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发表时间：
2000-05-01
影响因子：
2.8
通讯作者：
Cheng, C
中科院分区：
文献类型：
--
作者：
Shahar, Y;Cheng, C
我们描述了一个新的概念方法和相关的计算架构称为知识为基础的导航抽象的可视化和解释（KNAVE）。KNAVE是一个独立于领域的框架，专门用于以上下文敏感的方式对面向时间的原始数据和可以从这些数据中抽象出来的更高级别的基于间隔的概念进行解释，总结，可视化，解释和交互式探索。KNAVE领域无关的探索算子基于基于知识的时间抽象问题解决方法中定义的关系，该方法用于抽象数据，因此可以直接使用该方法所依赖的特定于领域的知识库。因此，领域特定的语义驱动领域无关的可视化和探索过程，数据通过过滤器的领域特定的知识。通过访问特定于域的时间抽象知识库和特定于域的面向时间的数据库，KNAVE模块使用户能够查询特定于域的时间抽象，并改变可视化的焦点，从而为不同的任务（可视化和探索）重复使用为抽象目的获得的相同的域模型。我们专注于这里的方法，但也描述了初步评估的KNAVE原型在医疗领域。我们的实验纳入了七个用户，一个大的医疗病人的记录，和三个复杂的时间查询，典型的基于指南的护理，用户需要回答和/或探索。初步试验的结果令人鼓舞。新的方法具有潜在的广泛的影响，规划，监测，解释，和面向时间的数据的交互式数据挖掘。
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.