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<pdf width="1500px">https://ceur-ws.org/Vol-1088/preface.pdf</pdf>
<pre>
Preface


Ubiquitous Data Mining(UDM) uses Data Mining techniques to extract useful
knowledge from data, namely when its characteristics reflect a World in Movement.
The goal of this workshop is to convene researchers (from both academia and in-
dustry) who deal with techniques such as: decision rules, decision trees, association
rules, clustering, filtering, learning classifier systems, neural networks, support vec-
tor machines, preprocessing, postprocessing, feature selection and visualization tech-
niques for UDM of distributed and heterogeneous sources in the form of a continuous
stream with mobile and/or embedded devices and related themes.
    This is the third workshop in the topic. We received 12 submissions that were
evaluated by 3 members of the Program Committee. The PC recommended accept-
ing 8 full papers and 2 Position Papers. We have a diverse set of papers focusing
from activity recognition, predicting taxis demand, trend mining to more theoretical
aspects of learning model rules from data streams. All papers deal with different
aspects of evolving data and/or distributed data.
    We would like to thank all people that make this event possible. First of all,
we thank authors that submit their work and the Program Committee for the work
in reviewing the papers, and proposing suggestions to improve the works. A final
Thanks to the IJCAI Workshop Chairs for all the support.




João Gama, Michael May, Nuno Marques, Paulo Cortez and Carlos A. Ferreira
Program Chairs




                                           I
Organization
Program Chairs:
João Gama, Michael May, Nuno Marques, Paulo Cortez and Carlos A. Ferreira


Organizing Chairs:
Manuel F. Santos, Pedro P. Rodrigues and Albert Bifet


Publicity Chair:
Carlos A. Ferreira


Organized in the context of the project Knowledge Discovery from Ubiquitous Data
Streams (PTDC/EIA-EIA/098355/2008). This workshop is funded by the ERDF -
European Regional Development Fund through the COMPETE Programme (oper-
ational programme for competitiveness) and by the Portuguese Government Funds
through the FCT (Portuguese Foundation for Science and Technology).


Program Committee:
   • Albert Bifet, Technical University of Catalonia, Spain
   • Alfredo Cuzzocrea, University of Calabria, Italy
   • André Carvalho, University of São Paulo (USP), Brazil
   • Antoine Cornuéjols, LRI, France
   • Carlos A. Ferreira, Institute of Engineering of Porto, Portugal
   • Eduardo Spinosa, University of São Paulo (USP), Brazil
   • Elaine Sousa, University of São Paulo, Brazil
   • Elena Ikonomovska, University St. Cyril & Methodius, Macedonia
   • Ernestina Menasalvas, Technical University of Madrid, Spain
   • Florent Masseglia, INRIA, France
   • Geoffrey Holmes, University of Waikato, New Zealand
   • Hadi Tork, LIAAD-INESC TEC, Portugal
   • Jesús Aguilar, University of Pablo Olavide, Spain
   • Jiong Yang, Case Western Reserve University, USA
   • João Gama, University of Porto, Portugal


                                        II
• João Gomes, I2R A*Star, Singapure

• João Mendes Moreira, University of Porto, Portugal

• José Ávila, University of Malaga, Spain

• Josep Roure, CMU, USA

• Manuel F. Santos, University of Minho, Portugal

• Mark Last, Ben Gurion University, Israel

• Matjaž Gams, Jožef Stefan Institute, Slovenia

• Michael May, Fraunhofer Bonn, Germany

• Min Wang, IBM, USA

• Miroslav Kubat, University of Miami, USA

• Mohamed Gaber, University of Plymouth, UK

• Myra Spiliopoulou, University of Magdeburg, Germany

• Nuno Marques, University of Nova Lisboa, Portugal

• Paulo Cortez, University of Minho, Portugal

• Pedro P. Rodrigues, University of Porto, Portugal

• Philip Yu, IBM Watson, USA

• Raquel Sebastião, University of Porto, Portugal

• Rasmus Pederson, Copenhagen Business School, Denmark

• Ricard Gavaldà, University of Barcelona, Spain

• Shonali Krishnaswamy, Monash University, Australia

• Xiaoyang S. Wang, University of Vermont, USA

• Ying Yang, Monash University, Australia




                                     III

</pre>