title: Visual Trends Analysis in Time-Varying Ensembles.
abstract: Visualization and analysis techniques play a key role in the discovery of relevant features in ensemble data. Trends, in the form of persisting commonalities or differences in time-varying ensemble datasets, constitute one of the most expressive feature types in ensemble analysis. We develop a flow-graph representation as the core of a system designed for the visual analysis of trends in time-varying ensembles. In our interactive analysis framework, this graph is linked to a representation of ensemble parameter-space and the ensemble itself. This facilitates a detailed examination of trends and their correlations to properties of input-space. We demonstrate the utility of the proposed trends analysis framework in several benchmark data sets, highlighting its capability to support goal-driven design of time-varying simulations.
authors.forename: Harald
authors.initials: H
authors.lastname: Obermaier
authors.forename: Kevin
authors.initials: K
authors.lastname: Bensema
authors.forename: Kenneth I
authors.initials: KI
authors.lastname: Joy