TU Darmstadt / ULB / TUbiblio

From Raw Data to Rich Visualization: Combining Visual Search with Data Analysis

May, Thorsten and Nazemi, Kawa and Kohlhammer, Jörn (2014):
From Raw Data to Rich Visualization: Combining Visual Search with Data Analysis.
Springer, Berlin, Heidelberg, New York, pp. 203-209, DOI: 10.1007/978-3-319-06755-1₁₆, [Book Section]

Abstract

Visual analytics is an interdisciplinary field of research at the boundary between data mining, statistics and visualization. Patterns and relations in the data complement a semantic representation of knowledge on a lower level of abstraction. One important goal of visual analytics is to find relations hidden in vast amounts of data, which can be turned into useful knowledge. Analysis needs to be "visual", because human's visual cognitive abilities are important for the identification and refinement of the analytical process. Further the results of the analysis have to be presented in a way to match the user's perspective on the proposed task. However, typical users are not experts in statistics or data mining. The challenge of visual analytics is to keep domain experts in charge of the analytical process while reducing the workload due to the complexity of the techniques. While search and analysis usually arc mentioned in different contexts, they are highly interdependent processes. In fact, every exploratory analysis is a search for new knowledge. In turn, this knowledge can be used to refine future searches by introducing new concepts or relations to draw from. This article will show how automated and visual methods can be combined to connect knowledge artifacts on multiple levels of abstraction.

Item Type: Book Section
Erschienen: 2014
Creators: May, Thorsten and Nazemi, Kawa and Kohlhammer, Jörn
Title: From Raw Data to Rich Visualization: Combining Visual Search with Data Analysis
Language: English
Abstract:

Visual analytics is an interdisciplinary field of research at the boundary between data mining, statistics and visualization. Patterns and relations in the data complement a semantic representation of knowledge on a lower level of abstraction. One important goal of visual analytics is to find relations hidden in vast amounts of data, which can be turned into useful knowledge. Analysis needs to be "visual", because human's visual cognitive abilities are important for the identification and refinement of the analytical process. Further the results of the analysis have to be presented in a way to match the user's perspective on the proposed task. However, typical users are not experts in statistics or data mining. The challenge of visual analytics is to keep domain experts in charge of the analytical process while reducing the workload due to the complexity of the techniques. While search and analysis usually arc mentioned in different contexts, they are highly interdependent processes. In fact, every exploratory analysis is a search for new knowledge. In turn, this knowledge can be used to refine future searches by introducing new concepts or relations to draw from. This article will show how automated and visual methods can be combined to connect knowledge artifacts on multiple levels of abstraction.

Series Name: Cognitive Technologies
Publisher: Springer, Berlin, Heidelberg, New York
Uncontrolled Keywords: Business Field: Visual decision support, Research Area: Human computer interaction (HCI), Visual analytics, Information visualization, Data mining
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1007/978-3-319-06755-1₁₆
Export:

Optionen (nur für Redakteure)

View Item View Item