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Topological Methods in Data Analysis and Visualization III

Theory, Algorithms, and Applications

  • Conference proceedings
  • © 2014

Overview

Editors:
  1. Peer-Timo Bremer

    1. Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, USA

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  2. Ingrid Hotz

    1. Comparative Visualization Group, Konrad-Zuse-Zentrum für Informationstechnik, Berlin, Germany

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  3. Valerio Pascucci

    1. School of Computing and SCI Institute, University of Utah, Salt Lake City, USA

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  4. Ronald Peikert

    1. Department of Computer Science, ETH Zürich, Zürich, Switzerland

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  • Covers the full range of cutting-edge research in the sector
  • Features work by the most prominent and respected researchers
  • Addresses key issues such as representing big data
  • Contains papers on the latest developments including fast homology computation
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (17 papers)

  1. Front Matter

    Pages i-x
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  2. Robust Topological Analysis

    1. Front Matter

      Pages 1-1
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    2. Robust Detection of Singularities in Vector Fields

      • Harsh Bhatia, Attila Gyulassy, Hao Wang, Peer-Timo Bremer, Valerio Pascucci
      Pages 3-18

    3. Interpreting Feature Tracking Through the Lens of Robustness

      • Primoz Skraba, Bei Wang
      Pages 19-37

    4. Simplification of Morse Decompositions Using Morse Set Mergers

      • Levente Sipeki, Andrzej Szymczak
      Pages 39-53

    5. Toward the Extraction of Saddle Periodic Orbits

      • Jens Kasten, Jan Reininghaus, Wieland Reich, Gerik Scheuermann
      Pages 55-69

  3. Efficient Computation of Topology

    1. Front Matter

      Pages 71-71
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    2. Computational Topology via Functional Programming: A Baseline Analysis

      • David Duke, Hamish Carr
      Pages 73-87

    3. Distributed Contour Trees

      • Dmitriy Morozov, Gunther H. Weber
      Pages 89-102

    4. Clear and Compress: Computing Persistent Homology in Chunks

      • Ulrich Bauer, Michael Kerber, Jan Reininghaus
      Pages 103-117

    5. Parallel Computation of Nearly Recurrent Components of Piecewise Constant Vector Fields

      • Nicholas Brunhart-Lupo, Andrzej Szymczak
      Pages 119-132

  4. Simplification, Approximation, and Distance Measures

    1. Front Matter

      Pages 133-133
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    2. Notes on the Simplification of the Morse-Smale Complex

      • David Günther, Jan Reininghaus, Hans-Peter Seidel, Tino Weinkauf
      Pages 135-150

    3. Measuring the Distance Between Merge Trees

      • Kenes Beketayev, Damir Yeliussizov, Dmitriy Morozov, Gunther H. Weber, Bernd Hamann
      Pages 151-165

    4. Topological Integrity for Dynamic Spline Models During Visualization of Big Data

      • Hugh P. Cassidy, Thomas J. Peters, Horea Ilies, Kirk E. Jordan
      Pages 167-183

  5. Time-Dependent Analysis

    1. Front Matter

      Pages 185-185
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    2. A Comparison of Finite-Time and Finite-Size Lyapunov Exponents

      • Ronald Peikert, Armin Pobitzer, Filip Sadlo, Benjamin Schindler
      Pages 187-200

    3. Development of an Efficient and Flexible Pipeline for Lagrangian Coherent Structure Computation

      • Siavash Ameli, Yogin Desai, Shawn C. Shadden
      Pages 201-215

    4. Topological Features in Time-Dependent Advection-Diffusion Flow

      • Filip Sadlo, Grzegorz K. Karch, Thomas Ertl
      Pages 217-231

  6. Applications

    1. Front Matter

      Pages 233-233
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Keywords

About this book

This collection of peer-reviewed conference papers provides comprehensive coverage of cutting-edge research in topological approaches to data analysis and visualization. It encompasses the full range of new algorithms and insights, including fast homology computation, comparative analysis of simplification techniques, and key applications in materials and medical science. The volume also features material on core research challenges such as the representation of large and complex datasets and integrating numerical methods with robust combinatorial algorithms.

Reflecting the focus of the TopoInVis 2013 conference, the contributions evince the progress currently being made on finding experimental solutions to open problems in the sector. They provide an inclusive snapshot of state-of-the-art research that enables researchers to keep abreast of the latest developments and provides a foundation for future progress. With papers by some of the world’s leading experts in topological techniques, this volume is a major contribution to the literature in a field of growing importance with applications in disciplines that range from engineering to medicine.

Editors and Affiliations

  • Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, USA

    Peer-Timo Bremer

  • Comparative Visualization Group, Konrad-Zuse-Zentrum für Informationstechnik, Berlin, Germany

    Ingrid Hotz

  • School of Computing and SCI Institute, University of Utah, Salt Lake City, USA

    Valerio Pascucci

  • Department of Computer Science, ETH Zürich, Zürich, Switzerland

    Ronald Peikert

Bibliographic Information

  • Book Title: Topological Methods in Data Analysis and Visualization III

  • Book Subtitle: Theory, Algorithms, and Applications

  • Editors: Peer-Timo Bremer, Ingrid Hotz, Valerio Pascucci, Ronald Peikert

  • Series Title: Mathematics and Visualization

  • DOI: https://doi.org/10.1007/978-3-319-04099-8

  • Publisher: Springer Cham

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: Springer International Publishing Switzerland 2014

  • Hardcover ISBN: 978-3-319-04098-1Published: 07 May 2014

  • Softcover ISBN: 978-3-319-38165-7Published: 23 August 2016

  • eBook ISBN: 978-3-319-04099-8Published: 22 April 2014

  • Series ISSN: 1612-3786

  • Series E-ISSN: 2197-666X

  • Edition Number: 1

  • Number of Pages: X, 279

  • Number of Illustrations: 29 b/w illustrations, 69 illustrations in colour

  • Topics: Visualization, Analysis, Topology, Geometry, Image Processing and Computer Vision, Pattern Recognition

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