Bottom–Up Modeling of Design Knowledge Evolution: Application to Circuit Design Community Characterization
Xiaowei Liu, Alex Doboli, Simona Doboli · IEEE Transactions on Computational Social Systems · 2020
Group learning offers novel yet intriguing opportunities to improve a community's effectiveness by obtaining insight into the emergence of new design problems and concepts. This article proposes a new computational model and the related algorithmic methods to characterize circuit design communities over time. The bottom-up model explains knowledge evolution using two operators, combination/improvement for knowledge expansion, and blocking for concept elimination. New metrics were proposed to describe the effect of the two operators. Experiments considered three large data sets of circuit designs (switched-capacitor filters, CMOS OpAmp/OTAs, and ΔΣ ADCs). Opportunities to improve the communities were discussed.