Visual Analytics is successfully employed for an integrated data analysis by means of combining visual and analytical methods. The starting point for current Visual Analytics tools and workflows is usually the readily available data set. Rarely though, Visual Analytics goes beyond the data set and also incorporates the data generating processes that have led to the data in the first place into the analysis. And indeed, in many use case scenarios, this is hardly possible, as these processes cannot be captured as data to be analyzed themselves. Yet for the applications, in which this is feasible, new opportunities and challenges open up. In this paper, we illustrate these opportunities and challenges by our efforts to bring together Visual Analytics and stochastic simulation for cell biological applications. The integration of both is possible, as the data generating process runs in silico and can thus be captured and analyzed alongside the mere simulation result. For this, we present solutions and tools, which permit Visual Analytics on all stages of this particular data generating process -- on the stages of the model, the experiment, the simulation runs, and a combination of all three.
Schulz, H.-J., Uhrmacher, A. M., & Schumann, H. (2011). Visual analytics for stochastic simulation in cell biology. In Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies - i-KNOW ’11 (p. 1). New York, New York, USA: ACM Press. https://doi.org/10.1145/2024288.2024345