GreenCC: A Hybrid Approach to Sustainably Validate Manufacturing Data in Industry 4.0 Environments

Simon Paasche, Sven Groppe

2023

Abstract

The era of big data streams forces companies to rethink their business models to gain competitive advantages. To fully make use of the collected information, data have to be available in high quality. With big data, the impact of information and communications technology (ICT) is also increasing. The extended use of ICT leads to an increase in energy consumption and thus also in the CO2 footprint, both of which in turn result in high costs. A tradeoff between making use of the data and reducing the resources required for data acquisition and validation arises. Our work investigates how data validation in smart manufacturing environments can be implemented in an energy-efficient and resource-saving way. Therefore, we present a combination of a light consistency checker (LightCC) and a full consistency checker (FullCC) which can be activated in periods with a high probability of defects. Our LightCC uses heuristics to predict missing messages and identifies time frames with an increased likelihood for further inconsistencies. In these periods, our FullCC can be activated to perform an accurate validation. We call our developed system green consistency checker (GreenCC).

Paper Citation

in Harvard Style

Paasche S. and Groppe S. (2023). GreenCC: A Hybrid Approach to Sustainably Validate Manufacturing Data in Industry 4.0 Environments. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 621-628. DOI: 10.5220/0012147900003541

in Bibtex Style

@conference{data23,
author={Simon Paasche and Sven Groppe},
title={GreenCC: A Hybrid Approach to Sustainably Validate Manufacturing Data in Industry 4.0 Environments},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={621-628},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012147900003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - GreenCC: A Hybrid Approach to Sustainably Validate Manufacturing Data in Industry 4.0 Environments
SN - 978-989-758-664-4
AU - Paasche S.
AU - Groppe S.
PY - 2023
SP - 621
EP - 628
DO - 10.5220/0012147900003541
PB - SciTePress