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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | IEEE Transactions on Visualization and Computer Graphics |
| Publisher | Institute of Electrical and Electronics Engineers |
| Geographical Reach | international |
| ISSN | 1077-2626 |
| Volume | 29 |
| Number | 8 |
| Page Range | 3441 - 3457 |
| Date | 2023 |
| Abstract Text | We present ManuKnowVis, the result of a design study, in which we contextualize data from multiple knowledge repositories of a manufacturing process for battery modules used in electric vehicles. In data-driven analyses of manufacturing data, we observed a discrepancy between two stakeholder groups involved in serial manufacturing processes: Knowledge providers (e.g., engineers) have domain knowledge about the manufacturing process but have difficulties in implementing data-driven analyses. Knowledge consumers (e.g., data scientists) have no first-hand domain knowledge but are highly skilled in performing data-driven analyses. ManuKnowVis bridges the gap between providers and consumers and enables the creation and completion of manufacturing knowledge. We contribute a multi-stakeholder design study, where we developed ManuKnowVis in three main iterations with consumers and providers from an automotive company. The iterative development led us to a multiple linked view tool, in which, on the one hand, providers can describe and connect individual entities (e.g., stations or produced parts) of the manufacturing process based on their domain knowledge. On the other hand, consumers can leverage this enhanced data to better understand complex domain problems, thus, performing data analyses more efficiently. As such, our approach directly impacts the success of data-driven analyses from manufacturing data. To demonstrate the usefulness of our approach, we carried out a case study with seven domain experts, which demonstrates how providers can externalize their knowledge and consumers can implement data-driven analyses more efficiently. |
| Digital Object Identifier | 10.1109/tvcg.2023.3279857 |
| Other Identification Number | merlin-id:24322 |
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| Keywords | Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Signal Processing, Software, Visual Analytics, Interactive Visual Data Analysis |
| Additional Information | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |