Toward effective insight management in visual analytics systems

Although significant progress has been made toward effective in-sight 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 ap-proach to leverage this problem around the concept of facts. Facts refer to patterns, relationships, or anomalies extracted from data un-der analysis. They are the direct products of visual exploration and permit construction of insights together with user’s 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 Manage-ment 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 litera-ture survey and user studies. We then propose a conceptual frame-work of fact management based upon this fact taxonomy. A con-crete scenario of visual sense making on real data sets illustrates how this FMF will work