Data mining prototyping knowledge graphs for design process insights

James Gopsill*, Lorenzo Giunta, Mark Goudswaard, Chris Snider, Ben Hicks

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)

Abstract

Advancements in prototyping knowledge capture have resulted in several graph-based schemas where nodes represent prototyping activities and individuals, and edges represent semantic relationships between prototypes (e.g. influence and involvement) defined by the design team. Their subsequent implementation in web-based applications has enabled the integration of both physical and virtual prototyping knowledge. The integration affords traceability, version control, and application of graph analysis to generate insights into the Engineering Design process and the schema validation studies reported in prior research have resulted in several open-access Prototyping Knowledge Graph datasets. This paper builds on prior research by contributing a data mining analysis of nine Prototyping Knowledge Graphs. The analysis followed the CRISP-DM process. The purpose was to elicit insights on prototyping behaviour within the design process. The results revealed nine findings. Five corroborated existing observations and four were new observations. In particular, data mining of the knowledge graphs showed sequence and type of knowledge generated from prototyping are unique to each design process. Also, the critical design path is dominated by physical prototypes.

Original languageEnglish
JournalJournal of Engineering Design
Early online date17 Jan 2024
DOIs
Publication statusE-pub ahead of print - 17 Jan 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • CRISP-DM
  • graph analysis
  • hackathons
  • knowledge graphs
  • Prototyping

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