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Published online by Cambridge University Press:  27 July 2021

Filippo Chiarello
Department of Energy, Systems, Territory and Construction Engineering, University of Pisa B4DS Lab - Business Engin
Elena Coli*
Department of Information Engineering, University of Pisa B4DS Lab - Business Engin
Vito Giordano
Department of Information Engineering, University of Pisa B4DS Lab - Business Engin
Gualtiero Fantoni
Department of Civil and Industrial Engineering, University of Pisa B4DS Lab - Business Engin
Andrea Bonaccorsi
Department of Energy, Systems, Territory and Construction Engineering, University of Pisa B4DS Lab - Business Engin
Coli, Elena, University of Pisa, Department of Information Engineering, Italy,


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Data, information and knowledge are strongly involved in Engineering Design (ED) process. Despite the crucial role played by data in the design process, there is a lack of studies about how different data are used and generated by the various phases of the ED process. This study is a first attempt to fill this gap by mapping which data types are involved in the different ED phases from a research perspective.

In order to achieve this objective, we used a methodology based on Text Mining. Firstly, we retrieve a corpus of scientific papers related to ED; then, we build two lexicons to recognize ED phases and data types; finally, we collect these entities within ED papers and map the relations between them.

The methodology application allows the building of a network graph for visualizing the relations among data lexicon and ED lexicon. Then, we investigate the specific relations among data types and ED phases by building a heatmap to investigate data types from 3 different perspective.

The insight coming from our analysis shows that ED studies have a great potential in the usage of many data sources, but also that there exist some gaps to be solved in order to reach a more effective data usage in the context of ED.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
The Author(s), 2021. Published by Cambridge University Press


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