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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 (
[EMAIL PROTECTED]
)
Vasant Honavar, Iowa State University (
[EMAIL PROTECTED]
)
Rajesh Parekh, Blue Martini Software (
[EMAIL PROTECTED]
)
Jihoon Yang, SRA International (
[EMAIL PROTECTED]
)
For additional details please visit the website at
http://www.cs.iastate.edu/~dcaragea/ICMLA.html
