A Semantic Approach for Big Data Exploration in Industry 4.0

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.This research was funded by the Spanish Ministry of Economy and Competitiveness under Grant No. FEDER/TIN2016-78011-C4-2-R and the Basque Government under Grant No. IT1330-19. The work of Víctor Julio Ramírez is funded by the Spanish Ministry of Economy and Competitiveness under contract with reference BES-2017-081193

A Semantic Approach for Big Data Exploration in Industry 4.0

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.Comment: Published version of paper: Idoia Berges, V\'ictor Julio Ram\'irez-Dur\'an, Arantza Illarramendi: A Semantic Approach for Big Data Exploration in Industry 4.0. Big Data Res. 25: 100222 (2021). DOI: 10.1016/j.bdr.2021.10022

Evaluation of industrial engineering students’ competencies for process improvement in hospitals

The failures to properly educate students about process improvement can be seen as major factor leading to increased risks of patient safety and increased wastes in hospital settings. The purpose of this research was two-fold: 1) to identify characteristics that explain the efficacy of Plan-Do-Study-Act (PDSA) based-tools while used by Industrial Engineering (IE) students on multidisciplinary teams in hospital; 2) to identify competencies needed by IEs for effective process improvement in hospital using PDSA based-tools. Exploratory mixed method design approach with survey study, unstructured interviews, and focus group discussions was used to collect the data. A regression analysis was used to identify PDSA based-tool characteristics perceived by IE students as instrumental for process improvement. Next, the abductive inference was applied to analyze qualitative data in order to investigate competencies needed for effective process improvement using PDSA based-tools.Using regression analysis, we found the brainstorming via visualization, recognizing root-cause(s) of the problem and selecting improvement measures via linking the process flow with task(s) characteristics to be the significant characteristics. From qualitative data analysis, we learned that IE students strived in technical analysis but lacked competencies in analyzing qualitative data needed for change implementation efforts. There is increasing evidence that success in achieving process improvement goals is at least partially attributable to implementation processes and contexts and not just to the nature of the technical solution. Therefore, IE students interested in working in hospitals must develop new competencies related to qualitative data analysis to manage change initiatives.Peer Reviewe

University of Windsor Graduate Calendar 2022 Fall

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University of Windsor Graduate Calendar 2023 Spring

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University of Windsor Graduate Calendar 2023 Winter

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