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Visual Search and Analysis in Complex Information Spaces - Approaches and Research Challenges

Landesberger, Tatiana von and Schreck, Tobias and Fellner, Dieter W. and Kohlhammer, Jörn (2012):
Visual Search and Analysis in Complex Information Spaces - Approaches and Research Challenges.
Springer, Berlin, Heidelberg, New York, pp. 45-67, DOI: 10.1007/978-1-4471-2804-5₄, [Book Section]

Abstract

One of the central motivations for visual analytics research is the so-called information overload - implying the challenge for human users in understanding and making decisions in presence of too much information 37. Visual-interactive systems, integrated with automatic data analysis techniques, can help in making use of such large data sets 35. Visual Analytics solutions not only need to cope with data volumes that are large on the nominal scale, but also with data that show high complexity. Important characteristics of complex data are that the data items are difficult to compare in a meaningful way based on the raw data. Also, the data items may be composed of different base data types, giving rise to multiple analytical perspectives. Example data types include research data compound of several base data types, multimedia data composed of different media modalities, etc. In this paper, we discuss the role of data complexity for visual analysis and search, and identify implications for designing respective visual analytics applications. We first introduce a data complexity model, and present current example visual analysis approaches based on it, for a selected number of complex data types. We also outline important research challenges for visual search and analysis we deem important.

Item Type: Book Section
Erschienen: 2012
Creators: Landesberger, Tatiana von and Schreck, Tobias and Fellner, Dieter W. and Kohlhammer, Jörn
Title: Visual Search and Analysis in Complex Information Spaces - Approaches and Research Challenges
Language: English
Abstract:

One of the central motivations for visual analytics research is the so-called information overload - implying the challenge for human users in understanding and making decisions in presence of too much information 37. Visual-interactive systems, integrated with automatic data analysis techniques, can help in making use of such large data sets 35. Visual Analytics solutions not only need to cope with data volumes that are large on the nominal scale, but also with data that show high complexity. Important characteristics of complex data are that the data items are difficult to compare in a meaningful way based on the raw data. Also, the data items may be composed of different base data types, giving rise to multiple analytical perspectives. Example data types include research data compound of several base data types, multimedia data composed of different media modalities, etc. In this paper, we discuss the role of data complexity for visual analysis and search, and identify implications for designing respective visual analytics applications. We first introduce a data complexity model, and present current example visual analysis approaches based on it, for a selected number of complex data types. We also outline important research challenges for visual search and analysis we deem important.

Publisher: Springer, Berlin, Heidelberg, New York
Uncontrolled Keywords: Forschungsgruppe Semantic Models, Immersive Systems (SMIS), Forschungsgruppe Visual Search and Analysis (VISA), Business Field: Visual decision support, Research Area: Generalized digital documents, Visual analysis, Visual search interfaces, Complex data
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/978-1-4471-2804-5₄
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