MEDIS: Analysis Methodology for Data with Multiple Complexities

Raluca Portase, Ramona Tolas, Rodica Potolea

2021

Abstract

Hidden and unexpected value can be found in the vast amounts of data generated by IoT devices and industrial sensors. Extracting this knowledge can help on more complex tasks such as predictive maintenance or remaining useful time prediction. Manually inspecting the data is a slow, expensive, and highly subjective task that made automated solutions very popular. However, finding the value inside Big Data is a difficult task with many complexities. We present a general preprocessing methodology (MEDIS- MEthdology for preprocessing Data with multiple complexitIeS) consisting of a set of techniques and approaches which address such complexities.

Paper Citation

in Harvard Style

Portase R., Tolas R. and Potolea R. (2021). MEDIS: Analysis Methodology for Data with Multiple Complexities. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR; ISBN 978-989-758-533-3, SciTePress, pages 191-198. DOI: 10.5220/0010655100003064

in Bibtex Style

@conference{kdir21,
author={Raluca Portase and Ramona Tolas and Rodica Potolea},
title={MEDIS: Analysis Methodology for Data with Multiple Complexities},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR},
year={2021},
pages={191-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010655100003064},
isbn={978-989-758-533-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR
TI - MEDIS: Analysis Methodology for Data with Multiple Complexities
SN - 978-989-758-533-3
AU - Portase R.
AU - Tolas R.
AU - Potolea R.
PY - 2021
SP - 191
EP - 198
DO - 10.5220/0010655100003064
PB - SciTePress