On May 4th, 2007 we will have Prof. Jiawei Han (the author of your textbook!) visiting our department.

Who is Dr. Han?
Jiawei Han
Database and Information Systems Research Lab.
Department of Computer Science
University of Illinois at Urbana-Champaign
www.cs.uiuc.edu/~hanj
 

Professor Jiawei Han has been working on research into data mining, data warehousing, database systems, data streams, spatial databases, and biological databases, with over 300 journal and conference publications. He has chaired or served in the program committees of major conferences and workshops in data mining and database systems. Besides serving on the editorial boards for several journals, he is the founding Editor-In-Chief of ACM Transactions on Knowledge Discovery from Data. He is an ACM Fellow and has received ACM SIGKDD Innovations Award (2004) and IEEE CS Technical Achievement Award (2005). His book "Data Mining: Concepts and Techniques" has been adopted by many universities worldwide.

1stt opportunity: Prof. Han will meet with students during my regular CS 530 class hours (10:00 - 11:00 a.m., Room: EPS 350) to answer some of students' questions. All students are encouraged to take a great advantage of this unique opportunity. You will be given a chance to ask any types of questions to one of the most known specialists in the data mining area! Use it to your advantage! :)

2nd opportunity: Later, afternoon (3:10 - 4:00 p.m., Room: 101 Roberts Hall ) - Dr. Han will give a talk to the College of Engineering on:  Mining and Searching Graphs in Biological Databases
 

Here is the abstract of Dr. Han's talk:
Recent research on pattern discovery has progressed from mining frequent itemsets and sequences to mining structured patterns
including trees, lattices, and graphs. As a general data structure, graph can model complicated relations among data with wide
applications in bioinformatics. However, mining and searching large graphs in graph databases is challenging due to the presence of an
exponential number of frequent subgraphs.
In this talk, we present our recent progress on developing efficient and scalable methods for mining and searching of graphs in large
biological databases. We first introduce gSpan, an efficient method for mining all the frequent graph patterns in graph databases, by
extension of a depth-first frequent pattern growth method, developed in our previous research. Then we introduce CloseGraph, an
efficient method for mining closed frequent graph patterns. A graph g is closed in a database if there exists no proper supergraph of g
that has the same support as g. After that, we introduce a graph indexing method, gIndex and a graph approximate searching method,
grafil, both taking advantages of frequent graph mining to construct a compact but highly effective graph index and perform similarity
search with such indexing structures. These methods not only facilitate mining and querying graph patterns in massive biological
datasets but also claim broad applications in other fields, including DB/OS systems and software engineering.