IoT data stream analytics
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EDITORIAL
IoT data stream analytics Albert Bifet 1,2 & João Gama 3
# Institut Mines-Télécom and Springer Nature Switzerland AG 2020
1 Introduction 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. * Albert Bifet [email protected] João Gama [email protected] 1
LTCI, Télécom Paris, IP Paris, Paris, France
2
University of Waikato, Hamilton, New Zealand
3
Laboratory of Artificial Intelligence and Decision Support, and Faculty of Economics, University of Porto, Porto, Portugal
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.
2 The selected papers We received several submissions of high interest. The review process helped to select the best ones, guaranteeing the quality of the form and
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