Osmar Zaiane, PhD
- B.Sc. Honours, Computer Science and Management, University of Tunis, 1988
- M.Sc., Electronics, Paris XI University, 1989
- M.Sc., Computer Science, Laval University, 1992
- Ph.D., Computing Science, Simon Fraser University, 1999
- Scientific Director of the Alberta Machine Intelligence Institute (Amii)
Data Mining, Web Mining, Text Mining, Machine Learning, Social Network Analysis, Content-Based Information Retrieval, Multimedia.
My current research interests are knowledge discovery from large databases and information retrieval. In particular, I am interested in data mining from the Internet and data mining from multimedia repositories. The research work I am conducting focuses on web mining related to the content of web documents, the structure of the hypertext documents, and the usage of the World Wide Web.
My research on Web Mining includes: (1) Meta Web, an architecture for abstracting web content and structure and facilitating resource discovery and implicit knowledge extraction; (2) WebML, a declarative query language for web mining; (3) Web access log mining for user browsing behaviour understanding and adaptive web site construction; (4) Analysis of web usage for e-commerce and automatic on-line catalogues.
I am also interested in Content-based Image Retrieval and visual asset management. My recent research has focused on pattern discovery from multimedia repositories such as image and video collections. The application of these techniques for medical imaging is of particular interest to me.
Another research interest is data visualization and manipulation for large data collections or for data mining and analysis results.
A continuation of CMPUT 174, revisiting topics of greater depth and complexity. More sophisticated notions such as objects, functional programming, and Abstract Data Types are explored. Various algorithms, including popular searching and sorting algorithms, are studied and compared in terms of time and space efficiency. Upon completion of this two course sequence, students from any discipline should be able to build programs to solve basic problems in their area, and will be prepared to take more advanced Computing Science courses. Prerequisite: CMPUT 174 or SCI 100. Credit cannot be obtained for CMPUT 175 if one already has credit for CMPUT 275, except with permission of the Department.