CFP

SPECIAL TECHNICAL SESSION ON LEARNING FROM DISTRIBUTED DATA AND KNOWLEDGE REPOSITORIES

The 2002 International Conference on Machine Learning and Applications (ICMLA'02 ) Monte Carlo Resort, Las Vegas, Nevada, USA June 24-27, 2002

INTRODUCTION

Many practical knowledge discovery tasks present several new challenges in Machine Learning. The data and knowledge repositories required in these applications tend to be large, physically distributed, autonomously managed, and rapidly evolving. Public datasets on the Internet, corporate databases maintained as a distributed collection of datamarts on the company intranet, medical data including patient history, repositories containing results of medical studies and treatment information for the different ailments are examples of some of the distributed data and knowledge repositories that are in use today.

Despite the tremendous advances in computing power and communications infrastructure, the currently well known framework of knowledge discovery from a centrally located data warehouse is not suitable in several applications. Accumulating data into a central data warehouse is severely limited by the available communication bandwidth. Even if the data is successfully assembled in a central data warehouse, the cost of the computing infrastructure required to mine such a large volumes of data can be prohibitive. The rapidly evolving nature of some or all of the data repositories that feed into data warehouse makes it difficult to keep the data warehouse up to date. If the distributed repositories are autonomously maintained then the questions of privacy and security of the data as it is transferred to a centralized warehouse become crucial.

The scenarios outlined above call for a new distributed learning framework that should take into account both theoretical aspects and practical challenges of learning in such environments. There has been a flurry of activity in the area of learning from distributed data and knowledge repositories. This technical session is geared to bring together researchers and practitioners areas such as machine learning, knowledge discovery and data mining, information extraction, information fusion, software agent systems and those working on related problems in databases and distributed computing. It is our hope that this session will facilitate an exchange of knowledge and ideas and foster further progress in this interesting and challenging field.

IMPORTANT DATES

Submission Deadline: MARCH 8, 2002 Notification of Acceptance: MARCH 21, 2002 Camera Ready Papers Due: APRIL 22, 2002

SESSION CHAIRS

Doina Caragea, Iowa State University ( dcaragea@iastate.edu ) Vasant Honavar, Iowa State University ( honavar@cs.iatate.edu ) Rajesh Parekh, Blue Martini Software ( rparekh@bluemartini.com ) Jihoon Yang, SRA International ( yangji@verdi.iisd.sra.com )

For additional details please visit the website at http://www.cs.iastate.edu/~dcaragea/ICMLA.html