Topological Analysis of LArge Scale Simulations (TALASS)

Tracking features and exploring their evolution over time has long been one of the most common analysis tasks for a variety of applications in science and technology. Usually, this proceeds in three steps: First, the feature-of-interest within a data set needs to be defined; Second, features should be correlated, i.e. tracked, between successive time steps; and Third, the resulting feature tracks need to be analyzed. Tracking graphs which show the evolution of features as a collection of feature tracks that split/merge over time are one of the most common approaches used to illustrate feature evolution over time.


A simple tracking graph showing the evolution of features across time. Within the graph, each set of nodes in a vertical column represents features in one time step and shows the “tracks” of each feature as it evolves: splits, merges or disappears.

In this work, we present a general and flexible analysis environment which enables interactive exploration of feature evolution in time-varying data sets, regardless of the underlying data type. This framework couples hierarchical feature definitions with progressive graph layout algorithms to provide an interactive exploration of dynamically constructed tracking graphs. By making use of appropriate abstractions for feature definitions and their correlations and by utilizing suitable visual metaphors, we design this framework for addressing the general task of understanding feature evolution over time.”



User Interface

Our visualization environment contains of three different views. The first two presenting general conceptual views of the time dependent feature hierarchies, the third presenting a more specialized view for feature embedding. Within our framework, data is always presented with respect to a user-defined focus time step that is processed first. Subsequently, data is extracted and presented for neighboring time steps in order of increasing distance. As such, all views designed for only a single time step, i.e. feature embedding view and feature hierarchy view, use the focus to determine their time step. Furthermore, all views coordinate parameters to provide a fully linked analysis environment.






Functionality & Other Details




We demonstrate the utility and generality of this framework using several large-scale scientific and non-scientific data sets. In particular, we make use of combustion, cosmology, ocean science, weather and plasma-surface interaction data sets in the scientific domain and social media, healthcare and image data sets within the non-scientific domain.



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