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 exible 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: 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 concept drift and d) the number of classes may evolve overtime. Therefore, e cient 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.
Adaptive data pre-processing and knowledge discovery
{ 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. Moamar Sayed-Mouchaweh
Albert Bifet Hamid Bouchachia
Jo~ao Gama Rita Paula Ribeiro