HypoVis: Modeling Hypotheses with Visual Analytics Methods to Analyze the Past and Forecast the Future
By miksch - Posted on November 21st, 2010
Time-oriented data is ubiquitous in many real-world problems from areas, like business, medicine, or ecology. Therefore, we consider it very important to propose novel methods for the analysis of time-oriented data. A relatively new approach to solve problems of data analysis is Visual Analytics. The analytical reasoning abilities of automated systems are combined with human reasoning and expert knowledge by the means of interactive visualization. The ultimate goal is gaining insight into the available data, and several important steps in between have been worked out that describe how this goal can be reached. Currently, the discipline mainly consists of two strands: The first one provides insights based on scientific models generated using automated analysis. The second one provides insights based on hypotheses generated using interactive visualizations. The strands are hardly connected in many current frameworks. Combining and intertwining these two strands in an interactive manner would improve the analytic power of the methods available to date. Our goal is the development of a Visual Analytics framework that supports the formulation of scientific models in a comprehensive Visual Analytics process. These models are then to be used for data assessment and forecasting. The most important part of our work is the model definition process using interactive visualizations. To reach this goal, we propose a data model capable of containing such information that can be used by several steps of the Visual Analytics process. Consequently, we go from several distinct process strands to an interactive and iterative process inside an integrated framework.We focus our definition of the data model on the important case of time-oriented data. Problems involving time-oriented data need special attention because time data is different from other kinds of data. Time has an inherent structure, most prominently seen in the calendar aspect of time being composed of smaller granularities, like years and seasons. As these granularities influence natural and social aspects found again in the data, explicitly harnessing the structures of time in data analysis methods can greatly improve the amount of information gained. Therefore, our goal is to includethe structure of time in the data model to ease its use in our Visual Analytics framework.