Topological Methods in Data Analysis and Visualization II

When scientists analyze datasets in a search for underlying phenomena, patterns or causal factors, their first step is often an automatic or semi-automatic search for structures in the data. Of these feature-extraction methods, topological ones stand out due to their solid mathematical foundation. Topologically defined structures—as found in scalar, vector and tensor fields—have proven their merit in a wide range of scientific domains, and scientists have found them to be revealing in subjects such as physics, engineering, and medicine.

Full of state-of-the-art research and contemporary hot topics in the subject, this volume is a selection of peer-reviewed papers originally presented at the fourth Workshop on Topology-Based Methods in Data Analysis and Visualization, TopoInVis 2011, held in Zurich, Switzerland. The workshop brought together many of the leading lights in the field for a mixture of formal presentations and discussion. One topic currently generating a great deal of interest, and explored in several chapters here, is the search for topological structures in time-dependent flows, and their relationship with Lagrangian coherent structures. Contributors also focus on discrete topologies of scalar and vector fields, and on persistence-based simplification, among other issues of note. The new research results included in this volume relate to all three key areas in data analysis—theory, algorithms and applications.

• Data Analysis
• Geometry
• Topology
• Visualization

• Inst. Computational Science, CAB G 65.1, ETH Zürich, Zürich, Switzerland

  Ronald Peikert

• , Dept. of Informatics, University of Bergen, Bergen, Norway

  Helwig Hauser

• , School of Computing, University of Leeds, Leeds, United Kingdom

  Hamish Carr

• , Computational Science, ETH Zürich, Zürich, Switzerland

  Raphael Fuchs

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