[Acpc-l] LEARNING FROM DISTRIBUTED DATA AND KNOWLEDGE REPOSITORIES - Call For Papers - Las Vegas, USA ...

[Hamid Arabnia]

Dear Colleagues:

We would appreciate your help in circulating the following CFP for the 
special technical session at ICMLA'02.

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Call For Papers

SPECIAL TECHNICAL SESSION ON
LEARNING FROM DISTRIBUTED DATA AND KNOWLEDGE REPOSITORIES 
<http://www.cs.iastate.edu/%7Edcaragea/ICMLA.html>

The 2002 International Conference on Machine Learning and Applications 
(ICMLA'02 <http://www.cs.csubak.edu/%7Eicmla/> )
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.

TOPICS

Topics of interest at this special technical session include but are not 
limited to:

    * Theoretical Foundations:
      Task and data decomposition, knowledge representation, learning
      operators, complexity.
    * Algorithms:
      Scalable, efficient, robust, parallel and distributed learning
      algorithms that can learn from partial schemas, combine multiple
      models learned on horizontal or vertical partitions of the data,
      and update the learned model quickly and effectively in the
      presence of every changing data.
    * Learning Agents:
      Capable of functioning autonomously, collaborating, communicating,
      and co-ordinating the learning task(s) among themselves.
    * Architecture:
      Requirements and protocols for data and knowledge representation,
      communication  between agents, network bandwidth.
    * Privacy and Security:
      The ability to access sensitive information located remotely, the
      distribution of  learning tasks among mobile autonomous agents,
      and the transmission of learned knowledge between different
      repositories raises legitimate security and privacy concerns which
      must be addressed.
    * Applications:
      Challenging new applications in science, engineering, medicine,
      and  business.


IMPORTANT DATES

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


SUBMISSION PROCEDURE

An electric version of previously unpublished work at most 6 pages in 
length including figures, tables, and references. Electronic versions of 
the paper in postscript or PDF format should be submitted via email to 
Doina Caragea at dcaragea@iastate.edu.  The first page should mention 
the Title, Author(s), Affiliation(s), Contact Author's Name, Mailing 
address, and E-mail address.  


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 )

All questions and inquiries about the technical session should be 
directed to Doina Caragea.