Showing papers by "Bum Chul Kwon published in 2014"

Knowledge Generation Model for Visual Analytics

Abstract: Visual analytics enables us to analyze huge information spaces in order to support complex decision making and data exploration. Humans play a central role in generating knowledge from the snippets of evidence emerging from visual data analysis. Although prior research provides frameworks that generalize this process, their scope is often narrowly focused so they do not encompass different perspectives at different levels. This paper proposes a knowledge generation model for visual analytics that ties together these diverse frameworks, yet retains previously developed models (e.g., KDD process) to describe individual segments of the overall visual analytic processes. To test its utility, a real world visual analytics system is compared against the model, demonstrating that the knowledge generation process model provides a useful guideline when developing and evaluating such systems. The model is used to effectively compare different data analysis systems. Furthermore, the model provides a common language and description of visual analytic processes, which can be used for communication between researchers. At the end, our model reflects areas of research that future researchers can embark on.

State-of-the-Art Report of Visual Analysis for Event Detection in Text Data Streams

Abstract: Event detection from text data streams has been a popular research area in the past decade. Recently, the evolution of microblogging and social network services opens up great opportunities for various kinds of knowledge-based intelligence activities which require tracking of real-time events. In a sense, visualizations in combination with analytical processes could be a viable method for such tasks because it can be used to analyze the sheer amounts of text streams. However, data analysts and visualization experts often face grand challenges stemming out of the ill-defined concept of event and various kinds of textual data. As a result, we have few guidelines on how to build successful visual analysis tools that can handle specific event types and diverse textual data sources. Our goal is to take the first step towards answering the question by organizing insights from prior research studies on event detection and visual analysis. In the scope of this report, we summarize the evolution of event detection in combination with visual analysis over the past 14 years and provide an overview of the state-of-the-art methods. Our investigation sheds light on various kinds of research areas that can be the most beneficial to the field of visual text event analytics.

VisJockey : Enriching Data Stories through Orchestrated Interactive Visualization

Abstract: Visualization has recently begun to extend its role into a communication medium for presentation and storytelling. With the advancement in web-based visualization technology (e.g., D3), we increasingly encounter an integration of interactive visualizations into data stories in news media, blog posts, etc. However, these stories usually do not provide enough guidance on how to interpret and manipulate the accompanied visualizations. Therefore, readers are often on their own in finding the right state and area of visualization authors intended to show to support their arguments. We introduce VisJockey, a technique that enables readers to easily access authors’ intended view through orchestrated visualization. To offload readers’ burden in making connections between the text and the visualization, VisJockey augments the visualization through highlight, annotation, and animation. We describe the main concept of VisJockey and present three example stories.

Uncertainty Propagation and Trust Building in Visual Analytics

Abstract: Visual analytics combines human and machine abilities to generate new knowledge from data. Within this process, uncertainty often plays an important role in hindering the sensemaking process and analysis tasks. On the machine side, uncertainty builds up from the data source level to the visual output. On the human side, these uncertainties often result in “lack of knowledge or trust” or “overtrust.” Such human’s biased interpretation can be resolved if we can measure uncertainties and users’ trust at each stage and provide proper mitigation in time. We believe that we can achieve this by tracing data provenance and analytic provenance accurately and reflecting them on the system output. Therefore, our first goal is to identify the roles of uncertainty and trust along the entire visual analytics knowledge generation process. In addition, we aim to capture how uncertainty and trust can be derived from data and analytic provenance. In this workshop, we introduce a framework that describes the roles of uncertainty and trust, and introduce open research questions with potential solutions.

A Review of Web-Based Dietary Interventions: From the Human-Computer Interaction Practitioners' Perspective

Abstract: Despite the popularity of web-based dietary interventions, there are few evidence-based, practical guidelines that help human-computer interaction HCI practitioners design new dietary intervention systems. We suspect that a lack of such guidelines is partly due to a chasm between two major research domains, healthcare and HCI. We believe that technologies developed in HCI are not used and evaluated by healthcare researchers, so we fail to accumulate experiences to develop guidelines. To assess the gap, we carefully selected 86 papers that employed and evaluated various web-based dietary interventions in both fields and analyzed general characteristics, behavior change strategies, intervention media, and research outcomes used in each paper. Through this review, we reaffirmed our belief about the discrepancies between healthcare and HCI, and additional findings helped us offer some suggestions to close the gap. We also identified several interesting patterns among behavior change strategies, intervention media, and outcomes that provide potential topics for future research. © 2012 Wiley Periodicals, Inc.

Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices

Abstract: Scatter plots are effective diagrams to visualize distributions, clusters and correlations in two-dimensional data space. For highdimensional data, scatter plot matrices can be formed to show all two-dimensional combinations of dimensions. Several previous approaches for exploration of large scatter plot spaces have focused on ranking and sorting scatter plot matrices based on global patterns. However, often local patterns are of interest for scatter plot exploration. We present a preliminary idea to explore the scatter plot space by identifying significant local patterns (also called motifs in this work). Based on certain clustering algorithms and image-based descriptors, we identify and group a set of similar local candidate motifs in a large scatter plot space.