Authors:
Ricardo Jesus
1
;
Mário Antunes
1
;
Pétia Georgieva
2
;
Diogo Gomes
1
and
Rui L. Aguiar
1
Affiliations:
1
Instituto de Telecomunicações, Universidade de Aveiro, Aveiro and Portugal
;
2
IEETA Universidade de Aveiro, Aveiro and Portugal
Keyword(s):
Stream Mining, Time Series, Machine Learning, IoT, M2M, Context Awareness.
Related
Ontology
Subjects/Areas/Topics:
Data Communication Networking
;
Enterprise Information Systems
;
Internet of Things
;
Sensor Networks
;
Software Agents and Internet Computing
;
Software and Architectures
;
Telecommunications
Abstract:
The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, hand in hand with this growth goes the increasing difficulty to manage and organize all this new information. In fact, it becomes increasingly difficult to properly evaluate IoT and M2M context-aware platforms. Currently, these platforms use advanced machine learning algorithms to improve and optimize several processes. Having the ability to test them for a long time in a controlled environment is extremely important. In this paper, we discuss two distinct methods to generate a data stream from a small real-world dataset. The first model relies on first order Markov chains, while the second is based on GANs. Our preliminiar evalution shows that both achieve sufficient resolution for most real-world scenarios.