Solving problems with visual analytics
Author(s) -
Daniel A. Keim,
Leishi Zhang
Publication year - 2011
Publication title -
kops (university of konstanz)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.1145/2024288.2024290
Subject(s) - visual analytics , computer science , data science , analytics , field (mathematics) , cultural analytics , data analysis , process (computing) , human–computer interaction , data visualization , visualization , artificial intelligence , semantic analytics , data mining , world wide web , the internet , operating system , pure mathematics , web modeling , mathematics
Never before in history data has been generated and collected in such high volumes as it is today. Keeping up to date with the flood of data, using standard tools for data analysis and exploration, is fraught with difficulty. Visual analytics seeks to provide people with better and more effective ways to understand and analyze large datasets, while also enabling them to act upon their findings immediately. The field integrates the analytic capabilities of the computer and the abilities of the human analyst, allowing novel discoveries and empowering individuals to take control of the analytical process. In this paper we present the challenges of visual analytics and exemplify them with a couple of application examples that illustrate the existing potential of current visual analysis techniques but also their limitations.
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