Visual Search and Analysis - Novel Techniques for Helping to Retrieve and Understand in Large and Complex Data Sets


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Charlista: 
Tobias Schreck
Fecha: 
20 Marzo, 2013 - 12:00
Sala: 
Auditorio DCC, 3er piso
Organización: 
DCC
Bio: 

Tobias Schreck is an Assistant Professor for Visual Analytics with the Department for Computer and Information Science at University of Konstanz, Germany. Between 2007 and 2011, he was a Postdoc researcher at Technische Universität Darmstadt, Germany, where he built and led a research group on visual search and analysis. He obtained a PhD in Computer Science in 2006, and a Master of Science degree in Information Engineering in 2002, both from University of Konstanz.

 

Tobias Schreck works in the areas of Visual Analytics, Information Visualization, and Digital Libraries. His research interests include visual search and analysis in time-oriented, high-dimensional, and 3D object data, with applications in data analysis and multimedia retrieval. He has published more than 70 scientific papers and served in various committees, including co-chairing the Eurographics Workshop on 3D Object Retrieval and being a workshop co-chair for the 2013 IEEE Conference on Visual Analytics Science and Technology. Tobias Schreck leads several third-party funded research projects, and is a partner in the recently installed EU FP7 project PRESIOUS which addresses similarity-based 3D object repair methods, focusing on Cultural Heritage data.

Abstract:

Advances in generation, storage and dissemination technologies lead to increasingly large data volumes becoming available in many application domains. Examples include corporate information systems used for business decisions; data libraries used by scientists; or 3D object repositories used in engineering.  The effective access, understanding and re-usage of large data from such repositories by users are important tasks. However, they are often hindered by the size and complexity of the contained information. In this talk we will discuss approaches for visual and content-based search and analysis in sets of complex (in the sense of un- or semi-structured) data types. By means of examples in 3D object data and scientific measurement data, we will show how appropriately defined similarity measures in combination with visual-interactive data representations can provide for effective exploration of large data sets. We will also detail quantitative approaches for evaluation in this context. We will conclude with outlining directions for future works in the area.

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