Sungahn Ko

WordBridge: Using Composite Tag Clouds in Node-Link Diagrams for Visualizing Content and Relations in Text Corpora

We introduce WordBridge, a novel graph-based visualization technique for showing relationships between entities in text corpora. The technique is a node-link visualization where both nodes and links are tag clouds. Using these tag clouds, WordBridge can reveal relationships by representing not only entities and their connections, but also the nature of their relationship using representative keywords for nodes and edges. In this paper, we apply the technique to an interactive web-based visual analytics environment---Apropos---where a user can explore a text corpus using WordBridge. We validate the technique using several case studies based on document collections such as intelligence reports, co-authorship networks, and works of fiction.

MarketAnalyzer: An Interactive Visual Analytics System for Analyzing Competitive Advantage Using Point of Sale Data

Competitive intelligence is a systematic approach for gathering, analyzing, and managing information to make informed business decisions. Many companies use competitive intelligence to identify risks and opportunities within markets. Point of sale data that retailers share with vendors is of critical importance in developing competitive intelligence. However, existing tools do not easily enable the analysis of such large and complex data. therefore, new approaches are needed in order to facilitate better analysis and decision making. In this paper, we present MarketAnalyzer, an interactive visual analytics system designed to allow vendors to increase their competitive intelligence. MarketAnalyzer utilizes pixel‐based matrices to present sale data, trends, and market share growths of products of the entire market within a single display. These matrices are augmented by advanced underlying analytical methods to enable the quick evaluation of growth and risk within market sectors. Furthermore, our system enables the aggregation of point of sale data in geographical views that provide analysts with the ability to explore the impact of regional demographics and trends. Additionally, overview and detailed information is provided through a series of coordinated multiple views. In order to demonstrate the effectiveness of our system, we provide two use‐case scenarios as well as feedback from market analysts.

Multi-aspect visual analytics on large-scale high-dimensional cyber security data

In this article, we present a visual analytics system, SemanticPrism, which aims to analyze large-scale high-dimensional cyber security datasets containing logs of a million computers. SemanticPrism visualizes the data from three different perspectives: spatiotemporal distribution, overall temporal trends, and pixel-based IP (Internet Protocol) address blocks. With each perspective, we use semantic zooming to present more detailed information. The interlinked visualizations and multiple levels of detail allow us to detect unexpected changes taking place in different dimensions of the data and to identify potential anomalies in the network. After comparing our approach to other submissions, we outline potential paths for future improvement.

Analyzing high-dimensional multivaríate network links with integrated anomaly detection, highlighting and exploration

This paper focuses on the integration of a family of visual analytics techniques for analyzing high-dimensional, multivariate network data that features spatial and temporal information, network connections, and a variety of other categorical and numerical data types. Such data types are commonly encountered in transportation, shipping, and logistics industries. Due to the scale and complexity of the data, it is essential to integrate techniques for data analysis, visualization, and exploration. We present new visual representations, Petal and Thread, to effectively present many-to-many network data including multi-attribute vectors. In addition, we deploy an information-theoretic model for anomaly detection across varying dimensions, displaying highlighted anomalies in a visually consistent manner, as well as supporting a managed process of exploration. Lastly, we evaluate the proposed methodology through data exploration and an empirical study.

Automated Box-Cox Transformations for Improved Visual Encoding

The concept of preconditioning data (utilizing a power transformation as an initial step) for analysis and visualization is well established within the statistical community and is employed as part of statistical modeling and analysis. Such transformations condition the data to various inherent assumptions of statistical inference procedures, as well as making the data more symmetric and easier to visualize and interpret. In this paper, we explore the use of the Box-Cox family of power transformations to semiautomatically adjust visual parameters. We focus on time-series scaling, axis transformations, and color binning for choropleth maps. We illustrate the usage of this transformation through various examples, and discuss the value and some issues in semiautomatically using these transformations for more effective data visualization.

Applying mobile device soft keyboards to collaborative multitouch tabletop displays: design and evaluation

We present an evaluation of text entry methods for tabletop displays given small display space allocations, an increasingly important design constraint as tabletops become collaborative platforms. Small space is already a requirement of mobile text entry methods, and these can often be easily ported to tabletop settings. The purpose of this work is to determine whether these mobile text entry methods are equally useful for tabletop displays, or whether there are unique aspects of text entry on large, horizontal surfaces that influence design. Our evaluation consists of two studies designed to elicit differences between the mobile and tabletop domains. Results show that standard soft keyboards perform best, even at small space allocations. Furthermore, occlusion-reduction methods like Shift do not yield significant improvements to text entry; we speculate that this is due to the low ratio of resolution per surface units (i.e., DPI) for current tabletops.

A survey on visual analysis approaches for financial data

Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploration. Often, the development of such systems requires researchers to collaborate with Financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in Financial domains with respect to real‐world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize Financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.

VASA: Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Societal Infrastructure.

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.

NLE-FFS: a flash file system with PRAM for non-linear editing

For efficient non-linear editing (NLE) operations, a flash file system should be designed considering three factors: data indexing, system calls and frame header updates. Based on the hybrid architecture of phase-change RAM (PRAM) and NAND flash, we introduce a non-linear editing flash file system (NLE-FFS) which is designed for mobile multimedia devices that support NLE. In the proposed file system, the following three features are proposed. First, new data indexing scheme is proposed that is not limited by page-alignment constraint. Not only does it deal effectively with large multimedia files, it also facilitates flexible data management. Second, new system calls are proposed that minimize re-write overhead due to NLE operations by updating a small amount of metadata. Finally, an H-data block is proposed to reduce the overhead caused by frame header updates. The H-data block is a PRAM region reserved for frame header data updates. It allows byte-level updates instead of page-level updates; hence, several bytes of frame headers can be effectively updated in this region. The experimental result of this study shows that only one second is enough for a cut operation on a five-minute video irrespective of the cutting position in NLE-FFS, whereas up to 118.7 seconds are required for the same video depending on the cutting position in YAFFS2.

A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of Aggregation

Real‐time microblogs can be utilized to provide situational awareness during emergency and disaster events. However, the utilization of these datasets requires the decision makers to perform their exploration and analysis across a range of data scales from local to global, while maintaining a cohesive thematic context of the transition between the different granularity levels. The exploration of different information dimensions at the varied data and human scales remains to be a non‐trivial task. To this end, we present a visual analytics situational awareness environment that supports the real‐time exploration of microblog data across multiple scales of analysis. We classify microblogs based on a fine‐grained, crisis‐related categorization approach, and visualize the spatiotemporal evolution of multiple categories by coupling a spatial lens with a glyph‐based visual design. We propose a transparency‐based spatial context preserving technique that maintains a smooth transition between different spatial scales. To evaluate our system, we conduct user studies and provide domain expert feedback.