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It uses a message passing model, and keeps intermediate data in memory to process graphs efficiently.
One important use of process graphs is to design systems that cannot deadlock.
Use-definition Analysis shows how event specifications imply data flows, which allow us to construct process graphs.
An interesting question then is whether or not the local optimization approach used in the previous proof gives an optimal solution on any type of process graphs.
The system is implemented using Spark GraphX tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time.
On several benchmarks, the PATH method was shown to be state-of-the-art in accuracy, and can easily process graphs with a few thousands vertices in a few hours on a modern desktop computer.
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The process graph model is a graph-theoretic approach originally developed for chemical process design applications.
A process graph representation of a HEN is proposed for formulating the optimization problem.
Process graph (P-graph) approach has been extended gradually to provide a friendly and fast optimum result for PNS problem.
Discrete-event simulation of hospitals typically specifies flow by means of a process graph through which patients are routed.
The process graph (P-graph) framework was introduced in the early 1990s as a graph theoretic approach to Process Network Synthesis PNSS) problems in chemical engineering.
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