Externalising abstract mathematical models
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Angular Brushing of Extended Parallel Coordinates
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Dynamic query tools for time series data sets: timebox widgets for interactive exploration
Information Visualization
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Interactive Visual Analysis of Families of Function Graphs
IEEE Transactions on Visualization and Computer Graphics
CMV '07 Proceedings of the Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization
State of the Art: Coordinated & Multiple Views in Exploratory Visualization
CMV '07 Proceedings of the Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization
SIMVIS: interactive visual analysis of large and time-dependent 3D simulation data
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Surveying the complementary role of automatic data analysis and visualization in knowledge discovery
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
Cross-Filtered Views for Multidimensional Visual Analysis
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Interactive Visual Analysis of Heterogeneous Scientific Data across an Interface
IEEE Transactions on Visualization and Computer Graphics
Visualization of Time-Oriented Data
Visualization of Time-Oriented Data
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Time-series data are regularly collected and analyzed in a wide range of domains. Multiple simulation runs or multiple measurements of the same physical quantity result in ensembles of curves which we call families of curves. The analysis of time-series data is extensively studied in mathematics, statistics, and visualization; but less research is focused on the analysis of families of curves. Interactive visual analysis in combination with a complex data model, which supports families of curves in addition to scalar parameters, represents a premium methodology for such an analysis. In this paper we describe the three levels of complexity of interactive visual analysis we identified during several case studies. The first two levels represent the current state of the art. The newly introduced third level makes extracting deeply hidden implicit information from complex data sets possible by adding data derivation and advanced interaction. We seamlessly integrate data derivation and advanced interaction into the visual exploration to facilitate an in-depth interactive visual analysis of families of curves. We illustrate the proposed approach with typical analysis patterns identified in two case studies from automotive industry.