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Learning to predict characteristics for engineering service projects

  • Lei Shi (a1), Linda Newnes (a1), Steve Culley (a1) and Bruce Allen (a2)
Abstract
Abstract

An engineering service project can be highly interactive, collaborative, and distributed. The implementation of such projects needs to generate, utilize, and share large amounts of data and heterogeneous digital objects. The information overload prevents the effective reuse of project data and knowledge, and makes the understanding of project characteristics difficult. Toward solving these issues, this paper emphasized the using of data mining and machine learning techniques to improve the project characteristic understanding process. The work presented in this paper proposed an automatic model and some analytical approaches for learning and predicting the characteristics of engineering service projects. To evaluate the model and demonstrate its functionalities, an industrial data set from the aerospace sector is considered as a the case study. This work shows that the proposed model could enable the project members to gain comprehensive understanding of project characteristics from a multidimensional perspective, and it has the potential to support them in implementing evidence-based design and decision making.

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Corresponding author
Reprint requests to: Lei Shi, Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK. E-mail: l.shi@bath.ac.uk
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AI EDAM
  • ISSN: 0890-0604
  • EISSN: 1469-1760
  • URL: /core/journals/ai-edam
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