Table of contents
Large-Scale Data Analysis: In-Situ and Distributed Analysis
Large-Scale Data Analysis: Efficient Representation of Large Functions
Multi-Variate Data Analysis: Structural Techniques
Multi-Variate Data Analysis: Classification and Visualization of Vector Fields
High-Dimensional Data Analysis: Exploration of High-Dimensional Models
About these proceedings
This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the field who examine challenges as well as detail solutions to the analysis of extreme scale data.
The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends.
Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-662-44900-4
- Copyright Information Springer-Verlag Berlin Heidelberg 2015
- Publisher Name Springer, Berlin, Heidelberg
- eBook Packages Mathematics and Statistics
- Print ISBN 978-3-662-44899-1
- Online ISBN 978-3-662-44900-4
- Series Print ISSN 1612-3786
- Series Online ISSN 2197-666X
- About this book