Data Flow Diagrams (DFD's) are widely used in industry to express requirements specifications. However, as used in practice, there has been no precise semantics for DFD's, let alone an incorporation of a model of time. In this paper, we augment the Formalized Data Flow Diagrams (FDFD's) defined in [LWBL96] by adding a deterministic (or stochastic) time behavior for the consumption of values from in--flows to processes and the production of values to the out--flows from processes. We call our new FDFD model Timed (or Stochastic) Data Flow Diagrams (TDFD's or SDFD's). We identify two factors in determining how time can affect the choice of how an FDFD can change state. The first factor has to do with when the decision is made as to which state transition will be next occur. The two possibilities are a Preselection Policy and a Race Policy. The other timing factor is the past history of an FDFD execution. We identify three alternatives: Resampling, Work Age Memory, and Enabling Age Memory..
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