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ORCID7th EuroVA@EuroVis 2016: Groningen, The Netherlands
- Natalia V. Andrienko, Michael Sedlmair:
7th International EuroVis Workshop on Visual Analytics, EuroVA@EuroVis 2016, Groningen, The Netherlands, June 6-7, 2016. Eurographics Association 2016, ISBN 978-3-03868-016-1
Best Paper
- Alexander Jäger, Sebastian Mittelstädt, Daniela Oelke, Sonja Sander, Axel Platz, Gies Bouwman, Daniel A. Keim:
Lessons on Combining Topology and Geography - Visual Analytics for Electrical Outage Management. 1-5
Multivariate Data Analysis
- Marco Angelini, Rocco Corriero, Fabrizio Franceschi, Marina Geymonat, Mario Mirabelli, Chiara Remondino, Giuseppe Santucci, Barbara Stabellini:
A Visual Analytics System for Mobile Telecommunication Marketing Analysis. 7-11 - Daniel Seebacher, Manuel Stein, Halldór Janetzko, Daniel A. Keim:
Patent Retrieval: A Multi-Modal Visual Analytics Approach. 13-17 - Bharathi Asokarajan, Ronak Etemadpour, June Abbas, Samuel J. Huskey
, Chris E. Weaver:
Visualization of Latin Textual Variants using a Pixel-Based Text Analysis Tool. 19-23 - Gunjan Sehgal, Geetika Sharma:
An Art-based Approach to Visual Analytics. 25-29
Temporal Data Analysis
- Jürgen Bernard, Eduard Dobermann, Markus Bögl, Martin Röhlig, Anna Vögele, Jörn Kohlhammer:
Visual-Interactive Segmentation of Multivariate Time Series. 31-35 - Patrick Köthur, Daniel Eggert, Andreas Schenk, Mike Sips:
Visual Analytics for Persistent Scatterer Interferometry: First Steps and Future Challenges. 37-41 - Simone Kriglstein, Margit Pohl, Stefanie Rinderle-Ma, Magdalena Stallinger:
Visual Analytics in Process Mining: Classification of Process Mining Techniques. 43-47 - Jürgen Bernard, David Sessler, Martin Steiger, Martin Spott, Jörn Kohlhammer:
Visual-Interactive Exploration of Relations Between Time-Oriented Data and Multivariate Data. 49-53
Networks
- Dang Tuan Nhon, Hong Cui, Angus G. Forbes:
MultiLayerMatrix: Visualizing Large Taxonomic Datasets. 55-59 - Jiachen Wang, Shiaofen Fang, Huang Li, Joaquín Goñi, Andrew J. Saykin, Li Shen:
Multigraph Visualization for Feature Classification of Brain Network Data. 61-65
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