A Four‐level Focus+Context Approach to Interactive Visual Analysis of Temporal Features in Large Scientific Data

Abstract

In this paper we present a new approach to the interactive visual analysis of time-dependent scientific databoth from measurements as well as from computational simulation -by visualizing a scalar function over time for each of tenthousands or even millions of sample points. In order to cope with overdrawing and cluttering, we introduce a new four-level method of focus+context visualization. Based on a setting of coordinated, multiple views (with linking and brushing), we integrate three different kinds of focus and also the context in every single view. Per data item we use three values (from the unit interval each) to represent to which degree the data item is part of the respective focus level. We present a color compositing scheme which is capable of expressing all three values in a meaningful way, taking semantics and their relations amongst each other (in the context of our multiple linked view setup) into account. Furthermore, we present additional image-based postprocessing methods to enhance the visualization of large sets of function graphs, including a texture-based technique based on line integral convolution (LIC). We also propose advanced brushing techniques which are specific to the timedependent nature of the data (in order to brush patterns over time more efficiently). We demonstrate the usefulness of the new approach in the context of medical perfusion data.

Result-Driven Exploration of Simulation Parameter Spaces for Visual Effects Design

“…These curves can be used to identify spatial regions with certain properties. Muigg et al [27] presented techniques for the visualization of a large set of these function graphs for applications such as breast tumor diagnosis. Woodring and Shen [39] applied clustering to time-activity curves to identify similar regions in space.…”

Interactive visual analysis of families of curves using data aggregation and derivation

“…This makes answering such questions possible, and it is at large the current state of the art [17]. Several brushes can be defined in the same or in different views.…”

“…This problem, too, can be solved by using more advanced interaction, or by computing several specific aggregates and using simple brushes. The well known similarity brush [10,17] represents the solution using advanced interaction. We propose two ways to perform similarity brushing: the user sketches the shape and then all similar curves are selected; or the user picks one of the curves and all curves similar to that one are selected.…”

Extensions of Parallel Coordinates for Interactive Exploration of Large Multi-Timepoint Data Sets

“…More recently, SimVis was applied to large dynamic datasets [12], but without significant involvement of traditional parallel coordinates.…”

“…We adopt the rendering approach of Muigg et al [12], where the histogram bins form a direct basis for drawing the primitives. Instead of having to draw a line for each data point, only a single primitive is drawn for each histogram bin.…”