Automatic Discovery of Design Task Structure Using Deep Belief Nets

With the arrival of cyber physical world and an extensive support of advanced IT infrastructure, nowadays it is possible to obtain the footprints of design activities through emails, design journals, change logs, and different forms of social data. In order to manage a more effective design process, it is essential to learn from the past and understand, for example, what design tasks are actually carried out, their interactions and how they impact each other. In this paper, a computational approach based on deep belief nets (DBN) is proposed to automatically uncover design tasks and quantify their interactions. A DBN topic modeling with real-valued units is to learn a set of intrinsic topic features from a simple word-frequency based input representation. Evaluated using a design email archive spanning for more than two years, the proposed approach has achieved a much higher accuracy in identifying design tasks compared to a prevailing approach.