DMKD 2004

The 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery

June 13, 2004, Maison de la Chimie, Paris, France

in conjunction with

ACM SIGMOD International Conference on Management of Data, 2004

sponsored by with corporate support from

Workshop Program

Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data


This is the 9th workshop on DMKD held annually in conjunction with ACM SIGMOD conference. The workshop aims to bring together data-mining researchers and practitioners with the goal of discussing the next generation of data-mining algorithms and tools. Rather than following a "mini-conference" format focusing on the presentation of polished research results, the DMKD workshop will foster an informal atmosphere, where researchers and practitioners can freely interact through short presentations and open discussions on their ongoing work as well as forward-looking research visions/experiences for future data-mining applications.

In addition to research on novel data-mining algorithms and experiences with innovative mining algorithms and applications, of particular interest in this year's DMKD workshop is the theme of "Data Mining and Information Integration".

Today, organizations are plagued with accessing and integrating disparate data. Very few organizations find that all of the information they need is readily available. Information is typically scattered throughout the organization in many locations, data stores, and formats. Data integration has become an urgent business problem. Extracting and integrating information from the Web is another area of great importance. With a huge amount of information publicly available, the Web offers an unprecedented opportunity for organizations to identify and extract useful information from diverse Web sources to provide value added services, to integrate Web information with their own data, and to discover business intelligence information about their competitors. Information integration is also critical for science, engineering and healthcare. To maximally automate the integration process, data mining and machine learning provides a key technology for discovering patterns or regularities in order to match database schemas, to clean the data and to identify, extract and combine data/information from diverse sources.

Topics of interest include (but are not limited to):


Submitted papers should not exceed 10 pages, single-spaced, single column, 12 point font, including all figures, tables, and references. The workshop accepts only electronic submission of papers in PDF format. Please email your paper to


Accepted papers will be included in ACM Digital Library. The final version of each paper must follow the ACM format guideline. The authors also need to sign the ACM copyright form. The format guideline and the copyright form will be sent to authors of accepted papers.


Submission deadline: March 29, 2004
Notification: April 26, 2004
Camera-ready due: May 14, 2004
Workshop: June 13, 2004


Gautam Das, Microsoft Research
Bing Liu, University of Illinois at Chicago
Philip S. Yu, IBM T.J. Watson Research Center


Charu Aggarwal, IBM T. J Watson Research Center
Venkatesh Ganti, Microsoft research
Johannes Gehrke, Cornell University
Robert Grossman, University of Illinois at Chicago
Dimitris Gunopulos, UC Riverside
Jiawei Han, University of Illinois at Urbana-Champaign
Vipin Kumar, University of Minnesota
Huan Liu, Arizona State University
Jian Pei, SUNY Buffalo
Raghu Ramakrishnan, University of Wisconsin-Madison
Kyuseok Shim, Seoul National University
Jaideep Srivastava, University of Minnesota
Ankur M. Teredesai, Rochester Institute of Technology
Alex Tuzhilin, New York University
Jamshid Vayghan, IBM T. J. Watson Research Center
Ke Wang, Simon Fraser University
Mohammed J. Zaki, Rensselaer Polytechnic Institute
Zhongfei Zhang, Binghamton University

Maintained by Bing Liu.