Developing new visual analysis methods to be integrated into simulation processes, focusing on the exploration of cell biological systems in space and time

大型随机模拟数据集的分析是一项具有挑战性的任务，特别是当空间和时间依赖性必须在不同的粒度级别进行研究时，就像分析细胞生物系统一样。我们通过可视化分析来解决这个问题。我们的主要重点将是交互式可视化方法和离散事件仿真过程的交织，以实现持续的视觉反馈和用户控制。因此，将在这两个领域制定创新战略。可视化：这包括多变量数据在空间和时间上的多层次可视化，并结合其来源和质量。这是指模型划分策略和组合和（重）这些新策略将有助于分析空间细胞生物系统的模拟数据和时间，而且还直接引导仿真过程，从而引导数据生成过程。因此，目前可用的可视化分析方法将通过两个重要方面得到丰富：· 与由真实的系统（湿实验室）产生的数据相比，源自离散事件模拟系统（干实验室）的细胞生物学数据的使用· 开发模拟方法作为分析推理的新来源

The analysis of large stochastic simulation data sets is a challenging task, especially when spatial and temporal dependencies have to be investigated at different levels of granularity, as is the case in analyzing cell biological systems. We approach this problem by means of Visual Analytics. Our main focus will be intertwining interactive visualization methods and discrete event simulation processes to allow for a sustained visual feedback and user control. Therefore, innovative strategies will be developed in both fields.Visualization: This includes the multi-level visualization of multi-variate data in space and time in combination with their provenance and quality.Simulation: This refers to model partitioning strategies and the combination and (re-) configuration of simulators induced automatically and by user interaction.These new strategies will help analyzing simulation data of cell biological systems in space and time, but also directly steering the simulation process, and thus the data generating process. Thereby, currently available approaches of Visual Analytics will be enriched by two important aspects:• Use of cell biological data originating from a discrete-event simulated system (drylab) in contrast to data produced by a real system (wet-lab)• Exploiting simulation methods as a new source for analytical reasoning