Ph.D. in Computer Science

Thesis Title: Resource and Knowledge Discovery from the Internet and Multimedia Repositories
University: Simon Fraser University

Burnaby, British Columbia, Canada
Research lab: Intelligent Database Systems Research Laboratory

School of Computing Science
Supervisors : Dr. Jiawei Han and Dr. Hassan Aït Kaci
Keywords : Data Mining, Web Mining, Knowledge Discovery from Large Databases, Content-Based Image Retrieval, Multimedia Data Mining. Query Language.
Digital form: http://www.cs.ualberta.ca/~zaiane/postscript/thesis/PhD/OsmarZaianePhD.pdf

Abstract/Summary:

There is a massive increase of information available on electronic networks. This profusion of resources on the World-Wide Web gave rise to considerable interest in the research community. Traditional information retrieval techniques have been applied to the document collection on the Internet, and a panoply of search engines and tools have been proposed and implemented. However, the effectiveness of these tools is not satisfactory. None of them is capable of discovering knowledge from the Internet. The Web is still evolving at an alarming rate. In a recent report on the future of database research known as the Asilomar Report, it has been predicted that in ten years from now, the majority of human information will be available on the World-Wide Web, and it has been observed that the database research community has contributed little to the Web thus far.

In this work we propose a structure, called a Virtual Web View, on top of the existing Web. Through this virtual view, the Web appears more structured, and common database technology is applied. The construction and maintenance of this structure is scalable and does not necessitate the large bandwidth current search engines technologies require. A declarative query language for information retrieval and networked tool programming is proposed that takes advantage of this structure to discover resources as well as implicit knowledge buried in the World-Wide Web.

Large collections of multimedia objects are being gathered for a myriad of applications. The use of on-line images and video streams is becoming commonplace. The World-Wide Web, for instance, is a colossal aggregate of multimedia artifacts. However, finding pertinent multimedia objects in a large collection is a difficult task. Images and videos often convey even more information than the text documents in which they are contained. Data mining from such a multimedia corpus can lead to interesting discoveries.

We propose the extraction of visual descriptors from images and video sequences for content-based visual media retrieval, and the construction of multimedia data cubes which facilitate multiple dimensional analysis of multimedia data, and the mining of multiple kinds of knowledge, including summarization, classification, and association, in image and video databases.

Vancouver, March 1999.

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