B
Bum Chul Kwon
Researcher at IBM
Publications - 66
Citations - 1940
Bum Chul Kwon is an academic researcher from IBM. The author has contributed to research in topics: Computer science & Visual analytics. The author has an hindex of 18, co-authored 49 publications receiving 1377 citations. Previous affiliations of Bum Chul Kwon include Purdue University & University of Konstanz.
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Journal ArticleDOI
Personas in online health communities
TL;DR: An online survey was developed to systematically investigate OHC personas and four personas emerged-Caretakers, Opportunists, Scientists, and Adventurers illustrating users' needs and requirements in OHC use.
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Visual Analytics for Multimodal Social Network Analysis: A Design Study with Social Scientists
TL;DR: This paper presents a design study that was conducted with several social scientist collaborators on how to support mSNA using visual analytics tools and devised a visual representation called parallel node-link bands (PNLBs) that splits modes into separate bands and renders connections between adjacent ones, similar to the list view in Jigsaw.
Journal ArticleDOI
Guidelines for Effective Usage of Text Highlighting Techniques
TL;DR: The pros and cons of different combinations as a design guideline to choose text highlighting techniques for text viewers are discussed and the results show that increasing font size works best as a single highlighting technique, and that there are significant visual interferences between some pairs of highlighting techniques.
Journal ArticleDOI
AxiSketcher: Interactive Nonlinear Axis Mapping of Visualizations through User Drawings
TL;DR: This paper introduces a technique to interpret a user's drawings with an interactive, nonlinear axis mapping approach called AxiSketcher, which enables users to impose their domain knowledge on a visualization by allowing interaction with data entries rather than with data attributes.
Proceedings ArticleDOI
State-of-the-Art Report of Visual Analysis for Event Detection in Text Data Streams
TL;DR: This report summarizes the evolution of event detection in combination with visual analysis over the past 14 years and provides an overview of the state-of-the-art methods.