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Fine-grained Visualization Pipelines and Lazy Functional Languages

机译:细粒度的可视化管道和惰性函数语言

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The pipeline model in visualization has evolved from a conceptual model of data processing into a widely used architecture for implementing visualization systems. In the process, a number of capabilities have been introduced, including streaming of data in chunks, distributed pipelines, and demand-driven processing. Visualization systems have invariably built on stateful programming technologies, and these capabilities have had to be implemented explicitly within the lower layers of a complex hierarchy of services. The good news for developers is that applications built on top of this hierarchy can access these capabilities without concern for how they are implemented. The bad news is that by freezing capabilities into low-level services expressive power and flexibility is lost. In this paper we express visualization systems in a programming language that more naturally supports this kind of processing model. Lazy functional languages support fine-grained demand-driven processing, a natural form of streaming, and pipeline-like function composition for assembling applications. The technology thus appears well suited to visualization applications. Using surface extraction algorithms as illustrative examples, and the lazy functional language Haskell, we argue the benefits of clear and concise expression combined with fine-grained, demand-driven computation. Just as visualization provides insight into data, functional abstraction provides new insight into visualization
机译:可视化中的管道模型已经从数据处理的概念模型演变为用于实现可视化系统的广泛使用的体系结构。在此过程中,引入了许多功能,包括分块中的数据流,分布式管道和需求驱动的处理。可视化系统始终以状态编程技术为基础,并且必须在复杂的服务层次结构的较低层中明确实现这些功能。对于开发人员而言,好消息是,在此层次结构之上构建的应用程序可以访问这些功能,而不必担心它们的实现方式。坏消息是,由于将功能冻结在低级服务中,因此丧失了表达能力和灵活性。在本文中,我们用一种编程语言来表达可视化系统,该语言更自然地支持这种处理模型。惰性函数语言支持细粒度的需求驱动处理,流的自然形式以及用于组装应用程序的类似管道的函数组合。因此,该技术似乎非常适合可视化应用程序。使用表面提取算法作为说明性示例,以及惰性函数语言Haskell,我们讨论了清晰简洁的表达式与细粒度的,需求驱动的计算相结合的好处。就像可视化提供对数据的洞察力一样,功能抽象也提供可视化的新洞察力

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