loading
Papers

Research.Publish.Connect.

Paper

Stream Generation: Markov Chains vs GANs

Topics: Analytics, Intelligence and Knowledge Engineering; Artificial Intelligence; Big Data Algorithm, Methodology, Business Models and Challenges; Context-awareness and Location-awareness ; Intelligent Systems for IoT and Services Computing ; Internet of Things; IoT Services and Applications; Performance Evaluation and Modeling ; User Evaluations and Case Studies

Authors: Ricardo Jesus 1 ; Mário Antunes 1 ; Pétia Georgieva 2 ; Diogo Gomes 1 and Rui Aguiar 1

Affiliations: 1 Instituto de Telecomunicações, Universidade de Aveiro, Aveiro and Portugal ; 2 IEETA Universidade de Aveiro, Aveiro and Portugal

ISBN: 978-989-758-369-8

Keyword(s): Stream Mining, Time Series, Machine Learning, IoT, M2M, Context Awareness.

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.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.175.191.168

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@conference{iotbds19,
author={Ricardo Jesus. and Mário Antunes. and Pétia Georgieva. and Diogo Gomes. and Rui L. 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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.