research-article

A Semantic Approach for Big Data Exploration in Industry 4.0

Authors:

Víctor Julio Ramírez-Durán,

Arantza IllarramendiAuthors Info & Claims

Volume 25, Issue C

https://doi.org/10.1016/j.bdr.2021.100222

Published: 15 July 2021 Publication History

Abstract

The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts.

In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis.

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Cited By

Bavaresco RRen YBarbosa JLi G(2024)An ontology-based framework for worker’s health reasoning enabled by machine learningComputers and Industrial Engineering10.1016/j.cie.2024.110310193:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.cie.2024.110310
Amara FDjezzar MHemam MTiwari SHafidi M(2023)Unlocking the Power of Semantic Interoperability in Industry 4.0: A Comprehensive OverviewKnowledge Graphs and Semantic Web10.1007/978-3-031-47745-4_7(82-96)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47745-4_7
Khademolqorani S(2022)Quality Mining in a Continuous Production Line based on an Improved Genetic Algorithm Fuzzy Support Vector Machine (GAFSVM)Computers and Industrial Engineering10.1016/j.cie.2022.108218169:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.cie.2022.108218

Index Terms

A Semantic Approach for Big Data Exploration in Industry 4.0
1. Information systems
  1. Data management systems
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Big Data Research

Big Data Research Volume 25, Issue C

Jul 2021

465 pages

ISSN:2214-5796

EISSN:2214-5796

Issue’s Table of Contents

Elsevier Inc.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 15 July 2021

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Cited By

Bavaresco RRen YBarbosa JLi G(2024)An ontology-based framework for worker’s health reasoning enabled by machine learningComputers and Industrial Engineering10.1016/j.cie.2024.110310193:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.cie.2024.110310
Amara FDjezzar MHemam MTiwari SHafidi M(2023)Unlocking the Power of Semantic Interoperability in Industry 4.0: A Comprehensive OverviewKnowledge Graphs and Semantic Web10.1007/978-3-031-47745-4_7(82-96)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47745-4_7
Khademolqorani S(2022)Quality Mining in a Continuous Production Line based on an Improved Genetic Algorithm Fuzzy Support Vector Machine (GAFSVM)Computers and Industrial Engineering10.1016/j.cie.2022.108218169:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.cie.2022.108218

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