This design study focuses on the analysis of a time sequence of categorical sequences. Such data is relevant for the geoscientific research field of landscape and climate development. It results from microscopic analysis of lake sediment cores. The goal is to gain hypotheses about landscape evolution and climate conditions in the past. To this end, geoscientists identify which categorical sequences are similar in the sense that they indicate similar conditions. Categorical sequences are similar if they have similar meaning (semantic similarity) and appear in similar time periods (temporal similarity). For data sets with many different categorical sequences, the task to identify similar sequences becomes a challenge. Our contribution is a tailored visual analysis concept that effectively supports the analytical process. Our visual interface comprises coupled visualizations of semantics and temporal context for the exploration and assessment of the similarity of categorical sequences. Integrated automatic methods reduce the analytical effort substantially. They (1) extract unique sequences in the data and (2) rank sequences by a similarity measure during the search for similar sequences. We evaluated our concept by demonstrations of our prototype to a larger audience and hands-on analysis sessions for two different lakes. According to geoscientists, our approach fills an important methodological gap in the application domain.

Abstract The π -calculus, in particular its stochastic version the stochastic π -calculus, is a common modeling formalism to concisely describe the chemical reactions occurring in biochemical systems. However, it remains largely unexplored how to transform a biochemical model expressed in the stochastic π -calculus back into a set of meaningful reactions. To this end, we present a two step approach of first translating model states to reaction sets and then visualizing sequences of reaction sets, which are obtained from state trajectories, in terms of reaction networks. Our translation from model states to reaction sets is formally defined and shown to be correct, in the sense that it reflects the states and transitions as they are derived from the continuous time Markov chain-semantics of the stochastic π -calculus. Our visualization concept combines high level measures of network complexity with interactive, table-based network visualizations. It directly reflects the structures introduced in the first step and allows modelers to explore the resulting simulation traces by providing both: an overview of a network’s evolution and a detail inspection on demand.

We present a Visual Analytics approach that addresses the detection of interesting patterns in numerical time series, specifically from environmental sciences. Crucial for the detection of interesting temporal patterns are the time scale and the starting points one is looking at. Our approach makes no assumption about time scale and starting position of temporal patterns and consists of three main steps: an algorithm to compute statistical values for all possible time scales and starting positions of intervals, visual identification of potentially interesting patterns in a matrix visualization, and interactive exploration of detected patterns. We demonstrate the utility of this approach in two scientific scenarios and explain how it allowed scientists to gain new insight into the dynamics of environmental systems.

Geoscientific modeling and simulation helps to improve our understanding of the complex Earth system. During the modeling process, validation of the geoscientific model is an essential step. In validation, it is determined whether the model output shows sufficient agreement with observation data. Measures for this agreement are called goodness of fit. In the geosciences, analyzing the goodness of fit is challenging due to its manifold dependencies: 1) The goodness of fit depends on the model parameterization, whose precise values are not known. 2) The goodness of fit varies in space and time due to the spatio-temporal dimension of geoscientific models. 3) The significance of the goodness of fit is affected by resolution and preciseness of available observational data. 4) The correlation between goodness of fit and underlying modeled and observed values is ambiguous. In this paper, we introduce a visual analysis concept that targets these challenges in the validation of geoscientific models - specifically focusing on applications where observation data is sparse, unevenly distributed in space and time, and imprecise, which hinders a rigorous analytical approach. Our concept, developed in close cooperation with Earth system modelers, addresses the four challenges by four tailored visualization components. The tight linking of these components supports a twofold interactive drill-down in model parameter space and in the set of data samples, which facilitates the exploration of the numerous dependencies of the goodness of fit. We exemplify our visualization concept for geoscientific modeling of glacial isostatic adjustments in the last 100,000 years, validated against sea levels indicators - a prominent example for sparse and imprecise observation data. An initial use case and feedback from Earth system modelers indicate that our visualization concept is a valuable complement to the range of validation methods.

Current visualization systems are typically based on the concept of interactive post-processing. This decoupling of data visualization from the process of data generation offers a flexible application of visualization tools. It can also lead, however, to information loss in the visualization. Therefore, a combination of the visualization of the data generating process with the visualization of the produced data offers significant support for the understanding of the abstract data sets as well as the underlying process. Due to the application-specific characteristics of data generating processes, the task requires tailored visualization concepts. In this work, we focus on the application field of simulating biochemical reaction networks as discrete-event systems. These stochastic processes generate multi-run and multivariate time-series, which are analyzed and compared on three different process levels: model, experiment, and the level of multi-run simulation data, each associated with a broad range of analysis goals. To meet these challenging characteristics, we present visualization concepts specifically tailored to all three process levels. The fundament of all three visualization concepts is a compact view that relates the multi-run simulation data to the characteristics of the model structure and the experiment. The view provides the visualization at the experiment level. The visualization at the model level coordinates multiple instances of this view for the comparison of experiments. At the level of multi-run simulation data, the views gives an overview on the data, which can be analyzed in detail in time-series views suited for the analysis goals. Although we derive our visualization concepts for one concrete simulation process, our general concept of tailoring the visualization concepts to process levels is generally applicable for the visualization of simulation processes.

Computational simulation is an established method to gain insight into cellular processes. As the resulting data sets are usually large and complex, visualization can play a significant role in data analysis. In this paper, we focus on the visualization of simulation output from the next sub-volume method, a spatial simulation algorithm. In addition to the spatial context of the simulation output, its heterogeneous data types, multiple variables, and the temporal context make high demands on the visualization. To cope with these challenging characteristics, we systematically explore possible visualization concepts with respect to these characteristics. From these findings, we derive our specific solution to visualize the data from the next sub-volume method, using a framework of multiple coordinated views that emphasize the spatial context of the data. Combining these views with a highly interactive user interface, the user is able to adapt the visualization to his current analysis goals and explore the data in its complexity.

In many application fields, the statistical properties of data sets are of great interest for data analysts. Since local variations can occur especially in large datasets, it is useful to visualize not only global values, but also the properties of user-defined subsets. Hence, we present in this paper a visualization of the statistical characteristics of subsets, with an emphasis on the temporal development. The visualization is coordinated with other views. They provide details about the data and allow for smooth brushing of subsets, a concept that introduces a continuous transition from data in focus to context data. Our approach accounts for smooth brushing in both the derivation and the visualization of statistical properties in order to visualize variations within subsets. To analyze differences between multiple subsets, our approach further integrates visualization concepts for the comparison of statistical properties. An application example from the simulation of biological systems demonstrates the benefits of our approach.