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P5:用于交互式数据分析和可视化的便携式渐进并行处理管道

P5: Portable Progressive Parallel Processing Pipelines for Interactive Data Analysis and Visualization.

作者信息

Li Jianping Kelvin, Ma Kwan-Liu

出版信息

IEEE Trans Vis Comput Graph. 2020 Jan;26(1):1151-1160. doi: 10.1109/TVCG.2019.2934537. Epub 2019 Aug 22.

Abstract

We present P5, a web-based visualization toolkit that combines declarative visualization grammar and GPU computing for progressive data analysis and visualization. To interactively analyze and explore big data, progressive analytics and visualization methods have recently emerged. Progressive visualizations of incrementally refining results have the advantages of allowing users to steer the analysis process and make early decisions. P5 leverages declarative grammar for specifying visualization designs and exploits GPU computing to accelerate progressive data processing and rendering. The declarative specifications can be modified during progressive processing to create different visualizations for analyzing the intermediate results. To enable user interactions for progressive data analysis, P5 utilizes the GPU to automatically aggregate and index data based on declarative interaction specifications to facilitate effective interactive visualization. We demonstrate the effectiveness and usefulness of P5 through a variety of example applications and several performance benchmark tests.

摘要

我们展示了P5,这是一个基于网络的可视化工具包,它结合了声明式可视化语法和GPU计算,用于渐进式数据分析和可视化。为了交互式地分析和探索大数据,渐进式分析和可视化方法最近应运而生。对逐步细化结果的渐进式可视化具有允许用户引导分析过程并尽早做出决策的优点。P5利用声明式语法来指定可视化设计,并利用GPU计算来加速渐进式数据处理和渲染。声明式规范可以在渐进式处理过程中进行修改,以创建用于分析中间结果的不同可视化。为了实现渐进式数据分析的用户交互,P5利用GPU根据声明式交互规范自动聚合和索引数据,以促进有效的交互式可视化。我们通过各种示例应用程序和几个性能基准测试来证明P5的有效性和实用性。

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