We propose a novel, comonadic approach to dataflow (stream-based) computation. This is based on the observation that both general and causal stream functions can be characterized as coKleisli arrows of comonads and on the intuition that comonads in general must be a good means to structure context-dependent computation. In particular, we develop a generic comonadic interpreter of languages for context-dependent computation and instantiate it for stream-based computation. We also discuss distributive laws of a comonad over a monad as a means to structure combinations of effectual and context-dependent computation. We apply the latter to analyse clocked dataflow (partial stream based) computation. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Uustalu, T., & Vene, V. (2005). The essence of dataflow programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3780 LNCS, pp. 2–18). https://doi.org/10.1007/11575467_2
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