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Chiarello, Filippo; Melluso, Nicola; Bonaccorsi, Andrea; Fantoni, Gualtiero

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The Engineering Design field is growing fast and so is growing the number of sub-fields that are bringing value to researchers that are working in this context. From psychology to neurosciences, from mathematics to machine learning, everyday scholars and practitioners produce new knowledge of potential interest for designers.

This leads to complications in the researchers? aims who want to quickly and easily find literature on a specific topic among a large number of scientific publications or want to effectively position a new research.

In the present paper, we address this problem by using state of the art text mining techniques on a large corpus of Engineering Design related documents. In particular, a topic modelling technique is applied to all the papers published in the ICED proceedings from 2003 to 2017 (3,129 documents) in order to find the main subtopics of Engineering Design. Finally, we analyzed the trends of these topics over time, to give a bird-eye view of how the Engineering Design field is evolving.

The results offer a clear and bottom-up picture of what Engineering design is and how the interest of researchers in different topics has changed over time.

Keywords:

Semantic data processing, Big data, Machine learning, Topic Modelling