Stream Generation: Markov Chains vs GANs

Ricardo Jesus, Mário Antunes, Pétia Georgieva, Diogo Gomes, Rui Aguiar

2019

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.

Paper Citation

in Harvard Style

Jesus R., Antunes M., Georgieva P., Gomes D. and Aguiar R. (2019). Stream Generation: Markov Chains vs GANs.In Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-369-8, pages 177-184. DOI: 10.5220/0007766501770184

in Bibtex Style

@conference{iotbds19,
author={Ricardo Jesus and Mário Antunes and Pétia Georgieva and Diogo Gomes and Rui Aguiar},
title={Stream Generation: Markov Chains vs GANs},
booktitle={Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2019},
pages={177-184},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007766501770184},
isbn={978-989-758-369-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Stream Generation: Markov Chains vs GANs
SN - 978-989-758-369-8
AU - Jesus R.
AU - Antunes M.
AU - Georgieva P.
AU - Gomes D.
AU - Aguiar R.
PY - 2019
SP - 177
EP - 184
DO - 10.5220/0007766501770184