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Call For Papers -- KDMG'05

From: Victor Robles <vrobles@fi.upm.es>
Date: Mon, 22 Nov 2004 13:09:58 -0500
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We apologize if you received multiple copies of this Call for Papers.
Please feel free to distribute it to those who might be interested.
Poland, 6 June - 9 June 2005 http://wic.ics.p.lodz.pl/awic/
Proceedings will be published by Springer Verlag
Nowadays, the process of data mining is one of the most important topics
in scientific and business problems. There is a huge amount of data that
can help to solve many of these problems. However, data is
geographically distributed in various locations and belongs to several
organizations. Furthermore, it is stored in different kind of systems
and it is represented in many formats. Different techniques have been
studied to make easier the data mining process in a distributed
However, data mining applications demand new alternatives in the field
of discovery, data placement, scheduling, resource management, and
transactional systems, among others. All these needs are not met by
traditional and homogeneous distributed systems. Grid computing has
appeared as a new technology, whose main challenge is the complete
integration of heterogeneous computing systems and data resources with
the aim of providing a global computing space. We consider that grid
provides a new framework in which data mining applications can be
successfully deployed.
One of the major goals of grid technology is to provide efficient access
to data. Grids provide access to distributed computing and data
resources, allowing data-intensive applications to improve significantly
data access, management and analysis. Grid systems responsible for
tackling and managing large amounts of data in geographically
distributed environments are usually named data grids. Generic data
management systems, and particularly data mining grids, involve a great
number of challenges.
On the other hand, the concept of knowledge grid is emerging as a new
stage in the development of current research in the semantic and
knowledge systems. Knowledge grids use knowledge-based methodologies,
including knowledge engineering tools,discovery and analysis techniques
such as data mining and machine learning, intelligent software agents,
mathematical modelling, simulation or planning. The knowledge grid will
provide intelligent guidance for decision-makers, through an agreed
knowledge representation and the provision of homogeneous access over
heterogeneous sources of information, ontologies and other kind of
TOPICS OF INTEREST include, but are not limited to:
.. Data Mining grids
.. Knowledge grids
.. Semantic grid and Semantic Web
.. Grid solutions for data-intensive applications
.. Grid infrastructures for data analysis
.. High-performance computing for data-intensive applications
.. Grid computing infrastructures, middleware and tools
.. Distributed Knowledge Management
.. Data analysis and management
.. Extracting knowledge from data grids
.. Agent-based management of data in distributed systems
.. Agent architectures for grid environments
Deadline for paper submission           December 4, 2004
Notification of acceptance              February 5, 2005
Deadline for camera-ready papers        February 28, 2005
Maria S. Perez
Facultad de Informatica
Universidad Politecnica de Madrid
Campus de Montegancedo S/N
28660 Boadilla del Monte
Madrid (Spain)
Phone: (+34) 91.336.73.80
Email: mperez@fi.upm.es
Pilar Herrero
Facultad de Informatica
Universidad Politecnica de Madrid
Campus de Montegancedo s/n
28660 Boadilla del Monte
Madrid (Spain)
Phone: (+34) 91.336.74.56
Email: pherrero@fi.upm.es
Victor Robles
Facultad de Informatica
Universidad Politecnica de Madrid
Campus de Montegancedo S/N
28660 Boadilla del Monte
Madrid (Spain)
Phone: (+34) 91.336.73.80
Email: vrobles@fi.upm.es
Received on Tuesday, 23 November 2004 04:01:45 UTC

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