Visual Analysis of Industrial Multivariate Time Series

Information

• Publication Type: Conference Paper
• Workgroup(s)/Project(s): not specified
• Date: September 2021
• ISBN: 9781450386470
• Series: VINCI 2021
• Publisher: Association for Computing Machinery
• Open Access: no
• Note: Best Short Paper Award
• Location: Potsdam, germany
• Lecturer: Maath Musleh
• Address: New York, NY, USA
• Event: VINCI 2021: The 14th International Symposium on Visual Information Communication and Interaction
• Editor: Karsten Klein, Michael Burch, Daniel Limberger, Matthias Trapp
• DOI: 10.1145/3481549.3481557
• Call for Papers: Call for Paper
• Booktitle: The 14th International Symposium on Visual Information Communication and Interaction
• Pages: 5
• Conference date: 6. September 2021 – 8. September 2021
• Pages: 1 – 5
• Keywords: time series data, unsupervised machine learning, visualization

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. 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.

Additional Files and Images

Weblinks

• https://dl.acm.org/doi/10.1145/3481549.3481557
• Entry in reposiTUm (TU Wien Publication Database)
• Entry in the publication database of TU-Wien
• DOI: 10.1145/3481549.3481557

BibTeX

@inproceedings{musleh_maath-2021-mam1,
  title =      "Visual Analysis of Industrial Multivariate Time Series",
  author =     "Maath Musleh and Angelos Chatzimparmpas and Ilir Jusufi",
  year =       "2021",
  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. 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.",
  month =      sep,
  isbn =       "9781450386470",
  series =     "VINCI 2021",
  publisher =  "Association for Computing Machinery",
  note =       "Best Short Paper Award",
  location =   "Potsdam, germany",
  address =    "New York, NY, USA",
  event =      "VINCI 2021: The 14th International Symposium on Visual
               Information Communication and Interaction",
  editor =     "Karsten Klein, Michael Burch, Daniel Limberger, Matthias
               Trapp",
  doi =        "10.1145/3481549.3481557",
  booktitle =  "The 14th International Symposium on Visual Information
               Communication and Interaction",
  pages =      "5",
  pages =      "1--5",
  keywords =   "time series data, unsupervised machine learning,
               visualization",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/musleh_maath-2021-mam1/",
}