情境感知流程网络

H. W. V. Dijk, H. Sips, E. Deprettere
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引用次数: 24

摘要

在工业领域,基于流处理的嵌入式系统通常通过使用流程网络(如 Kahn 流程网络)来建模和验证。Kahn 网络的一个优点是允许网络中的流程组件异步运行。然而,这些网络存在的一个问题是,异步干扰事件无法得到妥善处理,因为它们本质上是不确定的,因此会破坏网络的组成特性。我们建议用一个简单的不确定结构来扩展卡恩计算模型。我们将由此产生的网络称为上下文感知进程网络(CAPN)。我们证明,这些网络能够处理某些类别的事件,并且仍然可以简化为一类参数化的卡恩网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Context-aware process networks
In industry, embedded systems for stream-based processing are often modelled and verified by using process networks, such as Kahn process networks. An advantage of Kahn networks is that they allow asynchronous operation of process components in a network. A problem in these networks, however, is that asynchronously interfering events cannot be handled properly because they are intrinsically indeterminate and therefore destroy the compositional properties of the network. We propose to extend the Kahn model of computations with a simple indeterminate construct. We call the resulting network a context-aware process network (CAPN). We show that these networks are capable of handling certain classes of events and can still be reduced to a class of parametrised Kahn networks.
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