An engineering service project can be highly interactive, collaborative and distributed. The implementation of such projects needs to generate, utilise and share large amounts of data and heterogeneous digital objects. The information overload prevents the effective reuse of project data and knowledge, makes the understanding of project characteristics to become difficult. Towards solving these issues, this paper emphasised 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 dataset from the aerospace sector is considered by the case study. This work shows that the proposed model could enable the project members to gain comprehensive understanding of project characteristics from a multi-dimensional perspective, and it has the potential to support them in implementing evidence- based design and decision-making.
['Lei Shi', 'Linda Newnes', 'Steve Culley', 'Bruce Allen']
Learning to predict characteristics for engineering service projects