[Comp-neuro] Call for Papers - First Ubiquitous Knowledge Discovery Workshop (UKD08)

Pedro Pereira Rodrigues pprodrigues at liaad.up.pt
Thu Jun 5 13:03:54 CEST 2008


First Ubiquitous Knowledge Discovery Workshop (UKD08)
September 19, 2008 in Antwerp, Belgium
in conjuction with ECML/PKDD 2008 Conferences

http://wiki.kdubiq.org/UbiqKD_1stWorkshopPKDD08/index.php/Main/Home


Papers submission date: not later than June 16th, 2008

Papers should be sent electronically (postscript or pdf) to ukd at kdubiq.org 


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Over the last years, ubiquitous computing has started to create a new
world of small, heterogeneous, and distributed devices that have the
ability to sense, to communicate and interact in ad hoc or sensor
networks and peer2peer systems. These large scale distributed systems
have in many cases to interact in real-time with their users.

Knowledge Discovery in ubiquitous environments (KDubiq) is an emerging
area of research at the intersection of the two major challenges of
highly distributed and mobile systems and advanced knowledge discovery
systems. It aims to provide a unifying framework for systematically
investigating the mutual dependencies of otherwise quite unrelated
technologies employed in building next-generation intelligent systems:
machine learning, data mining, sensor networks, grids, P2P, data
stream mining, activity recognition, Web 2.0, privacy, user modelling
and others.

In a fully ubiquitous setting, the learning typically takes place in
situ, inside the small devices. Its characteristics are quite
different from the current mainstream data mining and machine
learning. Instead of offline-learning in a batch setting, sequential
learning, anytime learning, real-time learning, online learning
etc. under real-time constraints from ubiquitous and distributed data
is needed. Instead of learning from stationary distributions, concept
drift is the rule rather than the exception. Instead of large
stand-alone workstations, learning takes place in unreliable, highly
resource constrained environments in terms of battery power and
bandwidth.

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. 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 Statistics.



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