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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.
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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.
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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.