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 data both 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.
P. Muigg, J. Kehrer, S. Oeltze, H. Piringer, H. Doleisch, B. Preim, and H. Hauser, "A Four-level Focus+Context Approach to Interactive Visual Analysis of Temporal Features in Large Scientific Data," Computer Graphics Forum, vol. 27, iss. 3, p. 775–782, 2008.
[BibTeX]
In this paper we present a new approach to the interactive visual analysis of time-dependent scientific data both 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.
@ARTICLE {Muigg08four,
author = "Philipp Muigg and Johannes Kehrer and Steffen Oeltze and Harald Piringer and Helmut Doleisch and Bernhard Preim and Helwig Hauser",
title = "A Four-level Focus+Context Approach to Interactive Visual Analysis of Temporal Features in Large Scientific Data",
journal = "Computer Graphics Forum",
year = "2008",
volume = "27",
number = "3",
pages = "775--782",
month = "may",
abstract = "In this paper we present a new approach to the interactive visual analysis of time-dependent scientific data both 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.",
images = "images/muigg08_eurovis3.jpg, images/muigg08_eurovis1.jpg, images/muigg08_eurovis2.jpg",
thumbnails = "images/muigg08_eurovis3_thumb.jpg, images/muigg08_eurovis1_thumb.jpg, images/muigg08_eurovis2_thumb.jpg",
event = "EuroVis 2008",
location = "Eindhooven, Netherlands",
url = "//dx.doi.org/10.1111/j.1467-8659.2008.01207.x"
}