Multiple simulation runs using the same simulation model with different values of control parameters generate a large data set that captures the behavior of the modeled phenomenon. However, there is a conceptual and visual gap between the simulation model behavior and the data set that makes data analysis more difficult. We propose a simulation model view that helps to bridge that gap by visually combining the simulation model description and the generated data. The simulation model view provides a visual outline of the simulation process and the corresponding simulation model. The view is integrated in a Coordinated Multiple Views ;(CMV) system. As the simulation model view provides a limited display space, we use three levels of details. We explored the use of the simulation model view, in close collaboration with a domain expert, to understand and tune an electronic unit injector (EUI). We also developed analysis procedures based on the view. The EUI is mostly used in heavy duty Diesel engines. We were mainly interested in understanding the model and how to tune it for three different operation modes: low emission, low consumption, and high power. Very positive feedback from the domain expert shows that the use of the simulation model view and the corresponding ;analysis procedures within a CMV system represents an effective technique for interactive visual analysis of multiple simulation runs. We also developed new analysis procedures based on these results.
R ecent work has shown the great potential of interactive flow analysis by the analysis of path lines. The choice of suitable attributes, describing the path lines, is, however, still an open question. This paper addresses this question performing a statistical analysis of the path line attribute space. In this way we are able to balance the usage of computing power and storage with the necessity to not loose relevant information. We demonstrate how a carefully chosen attribute set can improve the benefits of state-ofthe art interactive flow analysis. The results obtained are compared to previously published work.
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.
Abstract. In this paper we present a novel approach to visualize irregularly occurring events. We introduce the event line view designed specifically for such events data (a subset of time dependent data). The event line view is integrated in a coordinated multiple views (CMV) system and linked with other conventional views to support interactive visual analysis. The main idea is to analyze events relative to two categorical attributes from a multidimensional multivariate dataset. Since we are interested in the categorical dimension we have also integrated and linked the tag cloud view in the CMV system. To the best of our knowledge this is the first integration of the tag cloud view in a CMV system. The tag cloud view can depict a ratio of the selected items versus the non-selected items. The proposed approach is illustrated using the VAST Challenge 2008 Geo-Spatial data set that contains data on interdiction or landing of illegal immigrants in the USA. It is a multivariate multidimensional dataset with irregular events that illustrates the potential and capabilities of the proposed approach and the developed CMV system.
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