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Visual analysis of blow molding machine multivariate time series data
ABSTRACT: The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273703/ https://www.ncbi.nlm.nih.gov/pubmed/35845181 http://dx.doi.org/10.1007/s12650-022-00857-4 |
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author | Musleh, Maath Chatzimparmpas, Angelos Jusufi, Ilir |
author_facet | Musleh, Maath Chatzimparmpas, Angelos Jusufi, Ilir |
author_sort | Musleh, Maath |
collection | PubMed |
description | ABSTRACT: The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results. GRAPHICAL ABSTRACT: [Image: see text] |
format | Online Article Text |
id | pubmed-9273703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-92737032022-07-12 Visual analysis of blow molding machine multivariate time series data Musleh, Maath Chatzimparmpas, Angelos Jusufi, Ilir J Vis (Tokyo) Regular Paper ABSTRACT: The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results. GRAPHICAL ABSTRACT: [Image: see text] Springer Berlin Heidelberg 2022-07-11 2022 /pmc/articles/PMC9273703/ /pubmed/35845181 http://dx.doi.org/10.1007/s12650-022-00857-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Regular Paper Musleh, Maath Chatzimparmpas, Angelos Jusufi, Ilir Visual analysis of blow molding machine multivariate time series data |
title | Visual analysis of blow molding machine multivariate time series data |
title_full | Visual analysis of blow molding machine multivariate time series data |
title_fullStr | Visual analysis of blow molding machine multivariate time series data |
title_full_unstemmed | Visual analysis of blow molding machine multivariate time series data |
title_short | Visual analysis of blow molding machine multivariate time series data |
title_sort | visual analysis of blow molding machine multivariate time series data |
topic | Regular Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9273703/ https://www.ncbi.nlm.nih.gov/pubmed/35845181 http://dx.doi.org/10.1007/s12650-022-00857-4 |
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