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Authors: Vitor Afonso Pinto 1 and Fernando Silva Parreiras 2

Affiliations: 1 Technology Department, Operational Technology for Base Metals South Atlantic, Vale, Carajas, Para, Brazil ; 2 Laboratory for Advanced Information Systems, FUMEC University, Rua do Cobre, Belo Horizonte, Brazil

Keyword(s): Data Science, Big Data, Framework, Mining Industry.

Abstract: Intending to be more and more data-driven, companies are leveraging data science upon big data initiatives. However, to reach a better cost-benefit, it is important for companies to understand all aspects involved in such initiatives. The main goal of this paper is to provide a framework that allows professionals from the mining industry to accurately describe data science upon big data. The following research question was addressed: ”Which essential components characterize an interdisciplinary framework for data science upon big data in mining industry?”. To answer this question, we will extend OntoDIVE ontology to create a framework capable of explaining aspects involved in such initiatives for the mining industry. As a result, this paper will present InfoMINDS - A Framework for Data Science upon Big Data Relating People, Processes and Technologies on Mining Industry. This paper will contribute to leveraging data science initiatives upon big data allowing application of OntoDIVE on real-case scenarios in mining industry. (More)

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Paper citation in several formats:
Pinto, V. and Parreiras, F. (2021). InfoMINDS: An Interdisciplinary Framework for Leveraging Data Science upon Big Data in Surface Mining Industry. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 784-791. DOI: 10.5220/0010484107840791

@conference{iceis21,
author={Vitor Afonso Pinto. and Fernando Silva Parreiras.},
title={InfoMINDS: An Interdisciplinary Framework for Leveraging Data Science upon Big Data in Surface Mining Industry},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2021},
pages={784-791},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010484107840791},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - InfoMINDS: An Interdisciplinary Framework for Leveraging Data Science upon Big Data in Surface Mining Industry
SN - 978-989-758-509-8
IS - 2184-4992
AU - Pinto, V.
AU - Parreiras, F.
PY - 2021
SP - 784
EP - 791
DO - 10.5220/0010484107840791
PB - SciTePress