Abstract
Unsteady scientific data is one of the top challenges in visualization, because a huge amount of information must be displayed. ϵ-machines are an information-theoretic concept; they compress the dynamics in the data set to a finite-state machine, in which nodes represent local flow patterns and edges represent transitions between them. Several enhancements to the fundamental ϵ-machine representation can help users identify interesting time intervals, analyze the evolution of unusual local dynamics, and track features over time. Automatically abstracting information from the original data is a first step toward knowledge-assisted visualization. Successive findings from analysis can help provide subsequent users with knowledge gained in earlier research, resulting in a knowledge-assisted system for the analysis of unsteady-flow features based on information theory. This article is part of a special issue on knowledge-assisted visualization.
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