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Novel cognitive radio platforms such as IMECs Cognitive Baseband RAdio (COBRA) should ensure the feasibility of multiple streams and their reconfigurability and scalability during run-time. The control over those tasks should be dedicated to a run-time controller that (re)allocates the resources on the platform. E.g., when user starts a new stream or the channel conditions change requiring switch to different modulation and coding scheme. The current transaction level models are too detailed for rapid exploration of all run-time options and the high-level data-flow frameworks (such as Kahn process networks) lack the dynamism and reconfigurability that is essential for the exploration. In this paper we propose the DAtaflow for Run-Time (DART), the high-level dynamic data-flow platform model framework, suited for rapid run-time control development. We sketch also how to use this framework to develop such a controller in the reactive and more challenging, proactive way.