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Visual Analysis of Industrial Multivariate Time Series
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS;DISA)ORCID iD: 0000-0002-9079-2376
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS)ORCID iD: 0000-0001-6745-4398
2021 (English)In: VINCI '21: Proceedings of the 14th International Symposium on Visual Information Communication and Interaction, ACM Press, 2021, article id 3Conference paper, Published paper (Refereed)
Abstract [en]

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. 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.

Place, publisher, year, edition, pages
ACM Press, 2021. article id 3
Keywords [en]
time series data, unsupervised machine learning, visualization
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science; Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-106810DOI: 10.1145/3481549.3481557Scopus ID: 2-s2.0-85120900563ISBN: 9781450386470 (electronic)OAI: oai:DiVA.org:lnu-106810DiVA, id: diva2:1591005
Conference
VINCI ’21, September 6–8, 2021, Potsdam, Germany
Note

This paper received the Best Short Paper award of VINCI '21, see https://vinci-conf.org/index.html.

Available from: 2021-09-04 Created: 2021-09-04 Last updated: 2022-06-13Bibliographically approved

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Musleh, MaathChatzimparmpas, AngelosJusufi, Ilir
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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf