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it - Information Technology

Methods and Applications of Informatics and Information Technology

Editor-in-Chief: Conrad, Stefan / Molitor, Paul

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Volume 57, Issue 2


Data analysis at scale

Rainer Gemulla
Published Online: 2015-03-27 | DOI: https://doi.org/10.1515/itit-2014-1077


My research focuses on methods to analyze and mine large datasets as well as their practical realizations and applications. The key question of interest to me is: How can we effectively and efficiently distill useful information from large, complex, and potentially noisy datasets? To approach this question, we are developing systems for scalable data analysis and data mining, for working with incomplete and noisy data, for data-intensive optimization, as well as for extracting structured information from natural-language text. This article highlights some of my work in these areas.

Keywords: Data analysis; data mining; parallel algorithms; information systems

ACM CCS: Information systems→Information systems applications→Data mining; Computing methodologies→Parallel computing methodologies

About the article

Rainer Gemulla

Rainer Gemulla is a professor for computer science at the University of Mannheim, Germany, which he joined in 2014. He obtained his PhD from the Technical University of Dresden, Germany, in 2008, and worked subsequently as a postdoctoral researcher at the IBM Almaden Research Center in San Jose, CA, USA, and as a senior researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany. Rainer's research focuses on data analysis and data mining techniques for efficiently extracting useful information from large, complex data collections. His work has been awarded with several awards, including multiple best-paper awards and a Google Focused Research Award. He serves as a junior fellow of the Gesellschaft für Informatik since September 2013.

Universität Mannheim, Data and Web Science Research Group, 68131 Mannheim, Germany

Accepted: 2015-01-28

Received: 2014-08-18

Published Online: 2015-03-27

Published in Print: 2015-04-28

Citation Information: it - Information Technology, Volume 57, Issue 2, Pages 130–132, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: https://doi.org/10.1515/itit-2014-1077.

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