IOTSTREAMING2017                                                          preface



Preface
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 pro-
cessing, 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 follow-
ing constraints: a) random access to observations is not feasible or it has high
costs, b) memory is small with respect to the size of data, c) data distribution
or phenomena generating the data may evolve over time, which is known as con-
cept drift and d) 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 dis-
tributed 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 poten-
tially 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 par-
ticipants 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


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IOTSTREAMING2017                                                       preface


        – Fault detection, isolation and prognosis,
        – Internet analytics
        – Decision Support Systems,
        – etc.
   This workshop accepted six papers which were carefully reviewed by at least
three program committe members.

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




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