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<pdf width="1500px">https://ceur-ws.org/Vol-2069/preface.pdf</pdf>
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STREAMEVOLV-2016                                                            preface



Preface
Motivation and focus
The volume of data is rapidly increasing due to the development of the tech-
nology of information and communication. This data comes mostly in the form
of streams. Learning from this ever-growing amount of data requires flexible
learning models that self-adapt over time. In addition, these models must take
into account many constraints: (pseudo) real-time processing, high-velocity, and
dynamic multi-form change such as concept drift and novelty. Consequently,
learning from streams of evolving and unbounded data requires developing new
algorithms and methods able to learn under the following constraints: -) random
access to observations is not feasible or it has high costs, -) memory is small with
respect to the size of data, -) data distribution or phenomena generating the
data may evolve over time, which is known as concept drift and -) the number
of classes may evolve overtime. Therefore, efficient data streams processing re-
quires particular drivers and learning techniques: Incremental learning in order
to integrate the information carried by each new arriving data; Decremental
learning in order to forget or unlearn the data samples which are no more useful;
Novelty detection in order to learn new concepts. It is worthwhile to emphasize
that streams are very often generated by distributed sources, especially with the
advent of Internet of Things and therefore processing them centrally may not
be efficient especially if the infrastructure is large and complex. Scalable and
decentralized learning algorithms are potentially more suitable and efficient.
    Aim and scope
This workshop welcomes novel research about learning from data streams in
evolving environments. It will provide the researchers and participants with a
forum for exchanging ideas, presenting recent advances and discussing challenges
related to data streams processing. It solicits original work, already completed
or in progress. Position papers are also considered. The scope of the workshop
covers the following, but not limited to:

   • Online and incremental learning

   • Online classification, clustering and regression
   • Online dimension reduction
   • Data drift and shift handling

   • Online active and semi-supervised learning
   • Online transfer learning
   • Adaptive data pre-processing and knowledge discovery
   • Applications in

   • Monitoring
        – Quality control
        – Fault detection, isolation and prognosis,
        – Internet analytics


                                                                                   i
STREAMEVOLV-2016                                                     preface


       – Decision Support Systems,
       – etc.

    This workshop accepted nine papers which were carefully reviewed by at
least three program committe members.
    Program Committee members

   • Edwin Lughofer, Johannes Kepler University of Linz, Austria

   • Sylvie Charbonnier, Universit Joseph Fourier-Grenoble, France
   • Bruno Sielly Jales Costa, IFRN, Natal, Brazil
   • Fernando Gomide, University of Campinas, Brazil
   • Jos A. Iglesias, Universidad Carlos III de Madrid, Spain

   • Anthony Fleury, Mines-Douai, Institut Mines-Tlcom, France
   • Teck Hou, Nanyang Technological University, Singapore
   • Angelov, Lancaster University, UK

   • Igor Skrjanc, University of Ljubljana, Slovenia
   • Zliobaite, Aalto University, Austria
   • Elaine Faria, Univ. Uberlandia, Brazil
   • Pechenizkiy, TU Eindonvhen, Netherlands

   • Sebastio, Univ. Aveiro, Portugal

18-22 September, 2017                          Moamar Sayed-Mouchaweh
Skopje, Macedonia                                    Hamid Bouchachia
                                                            João Gama
                                                     Rita Paula Ribeiro




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