We consider two classes of stream-based computations which admit taking linear combinations of execution runs: probabilistic sampling and generalized animation. The dataflow architecture is a natural platform for programming with streams. The presence of linear combinations allows us to introduce the notion of almost continuous transformation of dataflow graphs. We introduce a new approach to higher-order dataflow programming: a dynamic dataflow program is a stream of dataflow graphs evolving by almost continuous transformations. A dynamic dataflow program would typically run while it evolves. We introduce Fluid, an experimental open source system for programming with dataflow graphs and almost continuous transformations.
@article{arxiv.1601.00713,
title = {Almost Continuous Transformations of Software and Higher-order Dataflow Programming},
author = {Michael Bukatin and Steve Matthews},
journal= {arXiv preprint arXiv:1601.00713},
year = {2016}
}