Pak Chung Wong

Visual data mining

Method and system for finding targets within visual data, comprising receiving target object information. Generating a set of target object semantic attributes from the target object information. Identifying a plurality of candidate objects within visual data. Generating a set of low-level feature descriptors from the visual data for each candidate object. Generating from the set of low-level feature descriptors a set of candidate semantic attributes for each candidate object within the visual data. Identifying one or more portions of the visual data containing a candidate object, from the plurality of candidate objects, having a set of candidate object semantic attributes that match the set of target object semantic attributes. Providing an output indicating the identified one or more portions of the visual data.

Visual Analytics

The information revolution is upon us, and it is guaran-teed to change our lives and the way we conduct our daily business. The fact that we have to deal with not just the size but also the variety and complexity of this in-formation makes it a real challenge to survive the revolu-tion. Enter Visual Analytics, a contemporary and proven approach to combine the art of human intuition and the science of mathematical deduction to directly perceive patterns and derive knowledge and insight from them.

Geometry-Based Edge Clustering for Graph Visualization

Graphs have been widely used to model relationships among data. For large graphs, excessive edge crossings make the display visually cluttered and thus difficult to explore. In this paper, we propose a novel geometry-based edge-clustering framework that can group edges into bundles to reduce the overall edge crossings. Our method uses a control mesh to guide the edge-clustering process; edge bundles can be formed by forcing all edges to pass through some control points on the mesh. The control mesh can be generated at different levels of detail either manually or automatically based on underlying graph patterns. Users can further interact with the edge-clustering results through several advanced visualization techniques such as color and opacity enhancement. Compared with other edge-clustering methods, our approach is intuitive, flexible, and efficient. The experiments on some large graphs demonstrate the effectiveness of our method.

TOPIC ISLANDS/sup TM/-a wavelet-based text visualization system

We present a novel approach to visualize and explore unstructured text. The underlying technology, called TOPIC-O-GRAPHY/sup TM/, applies wavelet transforms to a custom digital signal constructed from words within a document. The resultant multiresolution wavelet energy is used to analyze the characteristics of the narrative flow in the frequency domain, such as theme changes, which is then related to the overall thematic content of the text document using statistical methods. The thematic characteristics of a document can be analyzed at varying degrees of detail, ranging from section-sized text partitions to partitions consisting of a few words. Using this technology, we are developing a visualization system prototype known as TOPIC ISLANDS to browse a document, generate fuzzy document outlines, summarize text by levels of detail and according to user interests, define meaningful subdocuments, query text content, and provide summaries of topic evolution.

Visualizing association rules for text mining

An association rule in data mining is an implication of the form X/spl rarr/Y where X is a set of antecedent items and Y is the consequent item. For years researchers have developed many tools to visualize association rules. However, few of these tools can handle more than dozens of rules, and none of them can effectively manage rules with multiple antecedents. Thus, it is extremely difficult to visualize and understand the association information of a large data set even when all the rules are available. This paper presents a novel visualization technique to tackle many of these problems. We apply the technology to a text mining study on large corpora. The results indicate that our design can easily handle hundreds of multiple antecedent association rules in a three-dimensional display with minimum human interaction, low occlusion percentage, and no screen swapping.

Organic data memory using the DNA approach

For very long-term storage and retrieval, encode information as artificial DNA strands and insert into living hosts. As vectors, bacteria, even some bugs and weeds, might be good for hundreds of millions of years.

The Top 10 Challenges in Extreme-Scale Visual Analytics

A team of scientists and researchers discusses the top 10 challenges in extreme-scale visual analytics (VA). The discussion covers applying VA technologies to both scientific and nonscientific data, evaluating the problems and challenges from both technical and social perspectives.

Visualizing sequential patterns for text mining

A sequential pattern in data mining is a finite series of elements such as A/spl rarr/B/spl rarr/C/spl rarr/D where A, B, C, and D are elements of the same domain. The mining of sequential patterns is designed to find patterns of discrete events that frequently happen in the same arrangement along a timeline. Like association and clustering, the mining of sequential patterns is among the most popular knowledge discovery techniques that apply statistical measures to extract useful information from large datasets. As out computers become more powerful, we are able to mine bigger datasets and obtain hundreds of thousands of sequential patterns in full detail. With this vast amount of data, we argue that neither data mining nor visualization by itself can manage the information and reflect the knowledge effectively. Subsequently, we apply visualization to augment data mining in a study of sequential patterns in large text corpora. The result shows that we can learn more and more quickly in an integrated visual data-mining environment.

Dynamic visualization of transient data streams

We introduce two dynamic visualization techniques using multidimensional scaling to analyze transient data streams such as newswires and remote sensing imagery. While the time-sensitive nature of these data streams requires immediate attention in many applications, the unpredictable and unbounded characteristics of this information can potentially overwhelm many scaling algorithms that require a full re-computation for every update. We present an adaptive visualization technique based on data stratification to ingest stream information adaptively when influx rate exceeds processing rate. We also describe an incremental visualization technique based on data fusion to project new information directly onto a visualization subspace spanned by the singular vectors of the previously processed neighboring data. The ultimate goal is to leverage the value of legacy and new information and minimize re-processing of the entire dataset in full resolution. We demonstrate these dynamic visualization results using a newswire corpus and a remote sensing imagery sequence.

A Novel Visualization Technique for Electric Power Grid Analytics

The application of information visualization holds tremendous promise for the electric power industry, but its potential has so far not been sufficiently exploited by the visualization community. Prior work on visualizing electric power systems has been limited to depicting raw or processed information on top of a geographic layout. Little effort has been devoted to visualizing the physics of the power grids, which ultimately determines the condition and stability of the electricity infrastructure. Based on this assessment, we developed a novel visualization system prototype, GreenGrid, to explore the planning and monitoring of the North American Electricity Infrastructure. The paper discusses the rationale underlying the GreenGrid design, describes its implementation and performance details, and assesses its strengths and weaknesses against the current geographic-based power grid visualization. We also present a case study using GreenGrid to analyze the information collected moments before the last major electric blackout in the Western United States and Canada, and a usability study to evaluate the practical significance of our design in simulated real-life situations. Our result indicates that many of the disturbance characteristics can be readily identified with the proper form of visualization.