Service Engineering and Management

Research on Service Engineering and Management

Major pillars of the Hong Kong economy are related to services such as finance, professional services, medicine, education and logistics. Those service systems are complex systems in which specific arrangements of people and technologies take actions that provide value for others. Systems are designed and built to provide and sustain services, yet because of their complexity and size, operations do not always go smoothly, and all interactions and results cannot be anticipated. As a result, systems engineers are trained to develop quantitative decision-making tools and methodologies for smooth, agile and resilient operations in data-intensive service systems such as finance, healthcare, and logistics.

 

 
 

Financial Digital Library
J. Yu, C.C. Yang and W. Lam
The Financial Digital Library being developed contains annual reports, financial news articles, and government documents that allows users from different places to access and search for the information they need based on concept space. We have a collection of annual reports from 249 Hong Kong public firms, real-time stock quotes, and a set of agents to support technical and fundamental analysis. We have also conducted a series of studies on how an electronic filing system can improve transparency of financial information transmission in Hong Kong.

Integration of OLAP and Multidimensional Inter Transaction Mining
J. Yu
Today’s markets are much more competitive and dynamic than ever before. Business enterprises prosper or fail according to the sophistication and speed of their information systems, and their ability to analyse and synthesize information using those systems. Integration of On-Line Analytical Processing (OLAP) and data mining is a promising direction since it facilitates interactive exploratory data analysis. The objective of this project aims at integrating OLAP and multidimensional inter-transaction data mining for large financial multidimensional databases.

Knowledge Discovery
W. Lam, H. Meng and J. Yu
This project focuses on automated or semi-automated learning from data and texts, and the transformation of learned theories into some knowledge representation formalisms. We expect to develop the theory and techniques for partial or full automation of the time-consuming process of expert knowledge elicitation through automatic knowledge discovery or learning from data. We aim not only at the accuracy and effectiveness of the learned information, but also at improving the level and depth of knowledge discovered.