Design of Dataflow
Design scripting is initially based on secondary syntactic mechanisms that control sequential executions of code. We call this model of computation control flow programming. On the other hand, dataflow represents a widespread alternative to control flow by utilizing concurrent streams of data. Dataflow programming languages are also considered as pedagogical tools in fields where the coders are not expert computer programmers. In the last decade, new parametric modeling tools introduced this conception to design scripting, shifting its practical and educational foundations. Dataflow programming represents a promising platform to study contemporary parametric modeling techniques in design while architects and designers increasingly use it as their algorithmic sketchbooks. This paper describes a purpose to reflect this shift in design education, pursuing four pedagogical patterns derived from in-class experiences; named “explication”, “kit of components”, “objectification”, and “re-generation”.
This paper describes an experiment on the design of dataflow. It suits well with some aspects of designing as a “curiosity-driven production of new knowledge” [after Nowotny 2011]. We might predict that future tools (such as programming languages) will continue to re-orient design; while design will continue to re-create tools.
This is why design education should remain experimented, discussed, and kept updated by academia. We should consider these chaotic in the sense that they are likely occurrences of infinite possibilities. Therefore, all individual feedbacks presented here are not subject to generalization. One pedagogical pattern could produce multiple design exercises, while one exercise could match multiple patterns. One of the most significant consequences (or risks, or potentials) of a data-driven design idea is the reduction (or redefinition) of design into data, and the design process into a “flow of data”.
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