This project aims to pursue strategic research in visual analysis. Key topics include the exploration of continuous parameter spaces, a comparative visualization of many categories, and the simultaneous visual analysis of multiple data tables.
Visual analysis has become a scientifically and economically important field. Multiple publications, a powerful software framework, and a growing number of related industry projects show that visual analysis is a key competence of VRVis. Building on this competence and technology, the strategic research project IVAN has two high-level goals: First, it seeks to address challenging research topics which further increase the visibility of visual analysis research of VRVis. Second, it will ensure the high degree of innovation of the visual analysis-related software technology of VRVis for the next few years.
IVAN addresses four concrete topics of research which have evolved as strategically relevant in discussions with both science and industry.
- The first topic addresses the exploration of continuous parameter spaces using multivariate prediction. Based on statistical methods, we envision to enable a local analysis of continuous, sampled parameter spaces and a prediction of quantitative target values in real-time. Novel visualization techniques will provide guidance for an efficient navigation to interesting regions of continuous parameter spaces and a local sensitivity analysis with respect to multiple parameters (see also the image). Another aspect concerns the visualization of the inherent uncertainty of predictions considering the different sources of uncertainty for different prediction methods.
- The second topic focuses on the generalization of data derivation as a powerful approach to coordinate multiple views. Today, data derivation is typically a static part of pre-processing. On the other hand, certain types of data derivation (e.g., interactive selection by brushing) have tightly been integrated in the analysis but are not general with respect to interaction and visualization concepts. We intend to describe a general model for interactive data derivation that seeks to combine two goals: 1) the specification of data derivation should be tightly integrated in the analysis process, and 2) the results should be handled as flexibly as possible. Based on an implementation of the model within visplore, we envision many applications that could benefit from a generalized approach, including similarity-based analysis, interactive data editing, and advanced aggregations of time-dependent data.
- The third topic is dedicated to a comparative visualization of many categories. Many current visualization approaches for analyzing categorical data rely on side-by-side comparison in small-multiple visualizations. While a small-multiple layout will also be the starting point of our approach, we intend to exceed the capabilities of current approaches in two important aspects: first, the envisioned approach should be tightly coupled to all other views, including multiple instances of itself. Our second goal is to explicitly visualize the difference of each plot with respect to a reference plot for common visualization types like bar charts, scatter plots, time series, and box plots. We expect such comparisons to be more precise than side-by-side comparisons in the case of many categories.
- The fourth topic deals with the visual analysis of many relational data tables. A relational data model is widely used in databases and data warehouses. On the other hand, many visualizations are limited to represent the data in terms of the raw data records that constitute one imported data table at a time. We intend to enable a simultaneous analysis of multiple relational data tables in visplore by linking views that refer to different tables. Another goal is to provide an overview of a database comprising many relational data tables, and to support to defining new pivot tables in an ad-hoc manner.
The work on these topics includes the conceptualization, prototype implementation within the system visplore, and the evaluation of results. Moreover, IVAN involves the scientific dissemination of results and scientific networking as well as the supervision of students.