Michael Chau, Ph.D.
Associate Professor
School of Business
Faculty of Business and Economics
The University of Hong Kong

Email: mchau |at| business |dot| hku |dot| hk
http://www.business.hku.hk/~mchau/

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Current Research Areas

Business Intelligence on the Web

As an excellent information source, the Internet provides significant opportunities for business intelligence analysis. Web content, outgoing links, and incoming links of a company’s Web site can all provide important insights about the company's business and "online communities". Although analysis of these contents and communities can provide useful signals for a company and information about its stakeholder groups, the manual analysis process can be very time-consuming for business analysts and consultants. My research addresses this problem by proposing a new design that integrates best-first search, backlink search, meta-search, and text mining techniques to facilitate users in performing such business intelligence analysis on the Web. Several research prototypes were developed and applied in various business contexts. Experiments were conducted to evaluate the effectiveness, efficiency, and user satisfaction of tools and the results showed that these tools were statistically more effective than the benchmark approaches.

Web Mining

The World Wide Web is the largest repository of information that can be easily accessed by many. Web mining research is the use of data mining and text mining as well as other similar techniques to discover resources, patterns, and knowledge from the Web and Web-related data (such as Web usage data or Web server logs). Web mining research can be classified into three categories: Web content mining, Web structure mining, and Web usage mining. In my research, Web content and structure mining techniques have been used in the development of Web search agents and Web portals. Blog mining also has been proposed to extract interesting patterns from blogs - most of them being frequently-updated personal online diaries. Such analyses have useful business and practical implications (e.g., marketing). Web usage mining has been performed on Web logs and search logs to reveal the information needs of Web users in order to improve the design of Web sites and search engines.

Data Mining

The goal of this research is to advance techniques in data mining for different types of data. For example, data uncertainty is often inherent in real world applications that require interaction with the physical world. In particular, data collected from external environments is often imprecise due to measurement inaccuracy, sampling discrepancy, outdated data sources, or other errors. However, this is often not taken into account in existing data mining algorithms. One research direction is to study the various issues of mining uncertain data, particularly with respect to data clustering. Another example is to study the application of data mining techniques to the Web. While most data mining techniques have been applied to Web data directly, I proposed that it is possible to incorporate various Web analysis metrics into data mining models. In particular, a Hopfield Net model was proposed to represent the Web's structure to tightly couple the mining techniques with the nature of the Web.

Security Informatics

Intelligence agencies such as the FBI are actively collecting and analyzing large amount of data to investigate terrorists' activities. Local law enforcement agencies have also been using data and information technology to fight against the criminal activities in their own jurisdictions. One challenge to these agencies is the difficulty in analyzing the large volumes of data involved in criminal and terrorist activities. My research addresses this problem by assisting these agencies in analyzing criminal data and online materials using various machine learning and visualization techniques.

IT Education

Advances in computer and Internet technologies have made it more and more important for information technology professionals to acquire experience in a variety of aspects, including new technologies, system integration, database administration, and project management. My research in this area has two main goals: (1) study the design and usage of various hands-on tools in classroom, and (2) study the use of machine learning algorithms to process digital teaching materials (e.g., lecture videos and slides) to assist e-learning.

Funding Sources

Michael Chau (c)