Toward Effective Insight Management in Visual Analytics Systems
Although significant progress has been made toward effective insight discovery in visual sense making approaches, there is a lack of effective and efficient approaches to manage the large amounts of insights discovered. In this paper, we propose a systematic approach to leverage this problem around the concept of facts. Facts refer to patterns, relationships, or anomalies extracted from data under analysis. They are the direct products of visual exploration and permit construction of insights together with users mental model and evaluation. Different from the mental model, the type of facts that can be discovered from data is predictable and application-independent. Thus it is possible to develop a general Fact Management Framework (FMF) to allow visualization users to effectively and efficiently annotate, browse, retrieve, associate, and exchange facts. Since facts are essential components of insights, it will be feasible to extend FMF to effective insight management in a variety of visual analytics approaches. Toward this goal, we first construct a fact taxonomy that categorizes various facts in multidimensional data and captures their essential attributes through extensive literature survey and user studies. We then propose a conceptual framework of fact management based upon this fact taxonomy. A concrete scenario of visual sense making on real data sets illustrates how this FMF will work.
Visual Analytics Decision Making Tazonomy Knowledge Management Multidimensional Visualization
Yang Chen Jing Yang William Ribarsky
Department of Computer Science UNC Charlotte
国际会议
IEEE Pacific Visualization Symposium 2009(2009 IEEE太平洋可视化研讨会)
北京
英文
49-56
2009-04-29（万方平台首次上网日期，不代表论文的发表时间）