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Visualizing Autonomous Warehouse Data Streams Through User-Centered Design
KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics. (Model-based engineering methodology and integrated engineering environments)ORCID iD: 0000-0002-8853-4159
Ericsson Research, Stockholm, Sweden.
2019 (English)In: Proceedings of The Fifth International Conference on Big Data, Small Data, Linked Data and Open DataALLDATA 2019, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper summarizes the work is done, which is carried out to develop and evaluate a dashboard design that visualizes a stream of data from different entities involved in autonomous warehouses, as a subset of cyber-physical systems. The dashboard is designed and developed through User- Centered Design (UCD) methodologies based on two iterations of feedback sessions with the stakeholders. During these sessions, semi-structured expert opinion interviews are conducted. The paper discusses the different stages involved in building the proposed dashboard design, the design decisions, the technical aspects of the libraries used, and the results of the feedback sessions towards the end of the project. It also presents the implemented dashboard as proof of development efforts and explains its different functionalities. The study concludes by evaluating the dashboard through the semi-structured interviews with the respective stakeholders and suggests features for further development.

Place, publisher, year, edition, pages
2019.
Keywords [en]
data visualization; information visualization; cyber-physical systems; user experience; user-centered design; expert opinion interviews; prototyping; data structures; supply chain; autonomous warehouse; intelligent agents; dashboard design
National Category
Engineering and Technology Embedded Systems Computer Engineering Human Computer Interaction
Research subject
Machine Design; Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-244863ISBN: 978-1-61208-700-9 (print)OAI: oai:DiVA.org:kth-244863DiVA, id: diva2:1292999
Conference
ALLDATA 2019, The Fifth International Conference on Big Data, Small Data, Linked Data and Open Data March 24, 2019 to March 28, 2019 - Valencia, Spain
Projects
SCOTT
Note

QC 20190304

Available from: 2019-03-01 Created: 2019-03-01 Last updated: 2019-03-04Bibliographically approved

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fulltext(1043 kB)32 downloads
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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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