IoT W+T

The volume of data is rapidly increasing due to the development of the technology 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. 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. This workshop is combined with a tutorial treating the same topic and will be presented in the same day.

​

Motivation and focus

The volume of data is rapidly increasing due to the development of the technology 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 requires 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

  •  Decision Support Systems,

  •  etc.

 

Submission and Review process

Regular and short papers presenting work completed or in progress are invited. Regular papers should not exceed 12 pages, while short papers are maximum 6 pages. Papers must be written in English and are to be submitted in PDF format online via the Easychair submission interface:

​

https://easychair.org/conferences/?conf=iotstreaming2017

 

Each submission will be evaluated on the basis of relevance, significance of contribution, quality of presentation and technical quality by at least two members of the program committee. All accepted papers will be included in the workshop proceedings and will be publically available on the conference web site. At least one author of each accepted paper is required to attend the workshop to present.

Important dates

Paper submission deadline: ***Monday, July 17, 2017 EXTENDED DEADLINE***
Paper acceptance notification: Monday, July 30, 2017
Paper camera-ready submission: Monday, August 7, 2017

Program Committee members (to be confirmed)

​

 

• Carlos Ferreira, LIAAD INESC Porto LA, ISEP, Portugal

• Edwin Lughofer, Johannes Kepler University of Linz, Austria

• Sylvie Charbonnier, Université Joseph Fourier-Grenoble, France

• Bruno Sielly Jales Costa, IFRN, Natal, Brazil

• Fernando Gomide, University of Campinas, Brazil

• José A. Iglesias, Universidad Carlos III de Madrid, Spain

• Anthony Fleury, Mines-Douai, Institut Mines-Télécom, France

• Teng Teck Hou, Nanyang Technological University, Singapore

• Plamen Angelov, Lancaster University, UK

• Igor Skrjanc, University of Ljubljana, Slovenia

• Indre Zliobaite, Aalto University, Austria

• Elaine Faria, Univ. Uberlandia, Brazil

• Mykola Pechenizkiy, TU Eindonvhen, Netherlands

• Raquel Sebastião, Univ. Aveiro, Portugal

Workshop Organizers

 

Moamar Sayed-Mouchaweh

Computer Science and Automatic Control Labs, High Engineering School of Mines, Douai, Francemoamar.sayed-mouchaweh@mines-douai.fr

Albert Bifet
Telecom-ParisTech; Paris, France
albert.bifet@telecom-paristech.fr

Hamid Bouchachia
Department of Computing & Informatics, University of Bournemouth, Bournemouth, UK
abouchachia@bournemouth.ac.uk

João Gama
Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal
jgama@fep.up.pt

Rita Ribeiro
Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal
rpribeiro@dcc.fc.up.pt