A Semantic Approach for Big Data Exploration in Industry 4.0
- URL: http://arxiv.org/abs/2401.09789v1
- Date: Thu, 18 Jan 2024 08:20:19 GMT
- Title: A Semantic Approach for Big Data Exploration in Industry 4.0
- Authors: Idoia Berges, V\'ictor Julio Ram\'irez-Dur\'an, Arantza Illarramendi
- Abstract summary: This paper proposes a semantic-based visual query system 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.
- Score: 0.05524804393257919
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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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