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Welcome |
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International Workshop on
September 17, 2007 - Warsaw, Poland |
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in conjuntion with the 18th European Conference on Machine Learning
(ECML) and the 11th European Conference on Principles and Practice
of Knowledge Discovery in Databases (PKDD).
The goal of this workshop is to promote an interdisciplinary forum
for researchers who deal with sequential learning, anytime
learning, real-time learning, online learning, etc. from ubiquitous
and distributed data streams. Distributed Learning from Data
Streams is a recent and increasing research area with challenging
applications and contributions from fields like Data Bases, Data
Mining, Machine Learning, and Visualization.
This workshop is a joint event with the tutorial in
State-of-the-art in Data Stream
Mining, and will be supported by the European Project KDUbiq-WG3. | ||
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Motivation and Goals
Advances in miniaturization and sensor technology lead to sensor
networks, collecting detailed spatio-temporal data about the
environment. How to learn from these distributed continuous
streaming data? Which are the main characteristics of a learning
algorithm acting in sensor networks? What are the relevant issues,
challenges, and research opportunities? Which emerging
applications?
The goal of this workshop is to convene researchers (from both
academia and industry) who deal with decision rules, decision trees,
association rules, clustering, filtering, preprocessing, post
processing, feature selection, visualization techniques, etc. from
distributed data streams and related themes. Special emphasis in
constrained algorithms designed to handle limited bandwidth, limited
computing and storage capabilities, limited battery power, and
specific network- -communication protocols.
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Workshop Chairs
João Gama
Mohamed Medhat Gaber
Jesús S. Aguilar-Ruiz |
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Publicity Chair
Pedro Pereira Rodrigues |