IoT data stream analytics
Résumé
The volume of IoT 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. Traditional one shot memory-based
learning methods trained offline from a static historic data cannot
cope with evolving data streams. This is because firstly, it is not
feasible to store all incoming data over time and secondly the
generated models become quickly obsolete due to data distribution changes, also known as “concept drift.” The basic assumption of offline learning is that data is generated by a stationary
process and the learning models are consistent with future data.
However, in multiple applications like IoT, web mining, social
networks, network monitoring, sensor networks, telecommunications, financial forecasting, etc., data samples arrive continuously as unlimited streams often at high speed. Moreover, the
phenomena generating these data streams may evolve over time.
In this case, the environment in which the system or the phenomenon generated the data is considered to be dynamic, evolving, or
non-stationary.
Learning methods used to learn from data generated by
dynamically evolving and potentially non-stationary processes must take into account many constraints: (pseudo) real-time
processing, high-velocity, and dynamic multiform change
such as concept drift and novelty. In addition in data streams
scenarios, the number of classes is often unknown in advance.
Therefore, new classes can appear at any time and they must
be detected, and the predictor structure must be updated.
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, particularly if the infrastructure is large
and complex. Scalable and decentralized learning algorithms
are potentially more suitable and efficient.
This special issue aims at discussing the problem of
learning from IoT data streams generated by evolving
non-stationary processes. It centers on the advances of
techniques, methods, and tools that are dedicated to
manage, exploit, and interpret data streams in nonstationary environments. In particular, it focuses on the
problems of modeling, prediction, and classification
based on learning from data streams.