OntoDIVE: An Ontology for Representing Data Science Initiatives upon Big Data Technologies

Vitor Pinto, Fernando Parreiras

Abstract

Intending to be more and more data-driven, companies are leveraging data science upon big data initiatives. However, in order to reach a better cost-benefit, it is important for companies to understand all aspects involved in such initiative. The main goal of this research is to provide an ontology that allows to accurately describe data science upon big data. The following research question was addressed: ”How can we represent a Initiative of data science upon big data?” To answer this question, we followed Knowledge Meta Processes guidelines from Ontology Engineering Methodology to build an artifact capable of explaining aspects involved in such initiatives. As a result, this study presents OntoDIVE, an ontology to explain interactions between people, processes and technologies in a data science initiative upon big data This study contributes to leverage data science upon big data initiatives, integrating people, processes and technologies. It confirms interdisciplinary nature of data science initiatives and enables organizations to draw parallels between data science results for a particular domain to their own domain. It also helps organizations to choose both frameworks and technologies based on their technical decision only.

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Paper Citation


in Harvard Style

Pinto V. and Parreiras F. (2020). OntoDIVE: An Ontology for Representing Data Science Initiatives upon Big Data Technologies.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 42-51. DOI: 10.5220/0009416500420051


in Bibtex Style

@conference{iceis20,
author={Vitor Pinto and Fernando Parreiras},
title={OntoDIVE: An Ontology for Representing Data Science Initiatives upon Big Data Technologies},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={42-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009416500420051},
isbn={978-989-758-423-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - OntoDIVE: An Ontology for Representing Data Science Initiatives upon Big Data Technologies
SN - 978-989-758-423-7
AU - Pinto V.
AU - Parreiras F.
PY - 2020
SP - 42
EP - 51
DO - 10.5220/0009416500420051