聚合平行坐标:将层次维度整合到平行坐标可视化中

K. Andrews, Majda Osmic, Gerhard Schagerl
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引用次数: 11

摘要

聚合平行坐标(APC)是标准平行坐标的扩展,它支持可视化和探索数值维度中的层次结构。这样的数据集可以出现在多个粒度的数据中,并且可以以某种方式(mean、sum、max)对这些数据进行分组或聚合,以形成更高层次的抽象。虽然现有的平行坐标技术可以用来可视化这些数据的各个维度,但它们没有提供交互式地扩展和折叠这些分层聚合的维度。在赛车工程中,专门的仿真软件用于在比赛中获得最佳性能的汽车设置。这样的模拟生成复杂的高维数据集,其中包含大量记录,其中许多数据集本质上是分层分组的。APC软件是在c#中作为Microsoft WPF用户控制库实现的。它已被集成到AVL的数据可视化工具SimBook中,并被用于探索赛车模拟的输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Aggregated parallel coordinates: integrating hierarchical dimensions into parallel coordinates visualisations
Aggregated Parallel Coordinates (APC) are an extension of standard parallel coordinates, which supports the visualisation and exploration of hierarchies within numerical dimensions. Such datasets can occur when data is available at several granularities and these can be grouped or aggregated in some way (mean, sum, max) to form higher levels of abstraction. While existing parallel coordinates techniques can be used to visualise individual dimensions of such data, they have no provision for interactively expanding and collapsing such hierarchically aggregated dimensions. In race car engineering, specialised simulation software is used to derive a car setup for optimal performance during a race. Such simulations generate complex, high-dimensional datasets with a large number of records, many of which are inherently hierarchically grouped. The APC software was implemented in C♯ as a Microsoft WPF user control library. It has been integrated into AVL's data visualisation tool called SimBook and is being used to explore the output of race car simulations.
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