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Distributed Contour Trees

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Topological Methods in Data Analysis and Visualization III

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Topological techniques provide robust tools for data analysis. They are used, for example, for feature extraction, for data de-noising, and for comparison of data sets. This chapter concerns contour trees, a topological descriptor that records the connectivity of the isosurfaces of scalar functions. These trees are fundamental to analysis and visualization of physical phenomena modeled by real-valued measurements.

We study the parallel analysis of contour trees. After describing a particular representation of a contour tree, called local–global representation, we illustrate how different problems that rely on contour trees can be solved in parallel with minimal communication.

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Notes

  1. 1.

    The algorithm of [7] is an exception. It computes many descriptors, Morse–Smale complexes of smaller portions of the domain. However, this information is not sufficient to resolve the Morse–Smale complex of the entire function. In particular, [7] ignores how one would use such a representation for the actual analysis. In our terminology, these descriptors are the local representations.

  2. 2.

    Sometimes authors make distinction between merge trees of super- and sub-level sets, calling the former join and the latter split trees. We prefer a unified terminology in this chapter.

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Acknowledgements

This work was supported by the Director, Office of Advanced Scientific Computing Research, Office of Science, of the U.S. DOE under Contract No. DE-AC02-05CH11231 (Lawrence Berkeley National Laboratory) through the grant “Topology-based Visualization and Analysis of High-dimensional Data and Time-varying Data at the Extreme Scale,” program manager Lucy Nowell.

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Correspondence to Dmitriy Morozov .

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Morozov, D., Weber, G.H. (2014). Distributed Contour Trees. In: Bremer, PT., Hotz, I., Pascucci, V., Peikert, R. (eds) Topological Methods in Data Analysis and Visualization III. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-04099-8_6

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