DocumentCode :
659560
Title :
Typograph: Multiscale spatial exploration of text documents
Author :
Endert, Alex ; Burtner, Russ ; Cramer, Nick ; Perko, Roland ; Hampton, Shawn ; Cook, K.
Author_Institution :
Pacific Northwest Nat. Lab. Richland, Richland, WA, USA
fYear :
2013
fDate :
6-9 Oct. 2013
Firstpage :
17
Lastpage :
24
Abstract :
Visualizing large document collections using a spatial layout of terms can enable quick overviews of information. These visual metaphors (e.g., word clouds, tag clouds, etc.) traditionally show a series of terms organized by space-filling algorithms. However, often lacking in these views is the ability to interactively explore the information to gain more detail, and the location and rendering of the terms are often not based on mathematical models that maintain relative distances from other information based on similarity metrics. In this paper, we present Typograph, a multi-scale spatial exploration visualization for large document collections. Based on the term-based visualization methods, Typograh enables multiple levels of detail (terms, phrases, snippets, and full documents) within the single spatialization. Further, the information is placed based on their relative similarity to other information to create the “near = similar” geographic metaphor. This paper discusses the design principles and functionality of Typograph and presents a use case analyzing Wikipedia to demonstrate usage.
Keywords :
data visualisation; rendering (computer graphics); text analysis; Wikipedia; document collection visualization; multiscale spatial exploration visualization; near-similar geographic metaphor; similarity metrics; space-filling algorithms; term-based visualization methods; terms location; terms rendering; terms spatial layout; text documents; typograph; visual metaphors; Context; Electronic publishing; Encyclopedias; Internet; Layout; Visualization; Visual analytics; sensemaking; spatialization; text analytics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data, 2013 IEEE International Conference on
Conference_Location :
Silicon Valley, CA
Type :
conf
DOI :
10.1109/BigData.2013.6691709
Filename :
6691709
Link To Document :
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