时间隧道:三维可视化工具及其三维平行坐标方面

Y. Okada
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引用次数: 4

摘要

本文介绍了一种三维可视化工具“时间隧道”,并通过具体的可视化实例将其描述为三维平行坐标。Time-tunnel最初是一个多维数据可视化工具,它被扩展为支持并行坐标,称为PCTT(Parallel Coordinates version of Time-tunnel)。此外,由于其三维平行坐标方面,增加了2Dto2D可视化功能。尽管PCTT可以可视化网络数据,因为IP数据包由许多属性组成,而且这种多属性数据可以使用并行坐标进行可视化,但2Dto2D可视化功能可以更有效地可视化看似网络攻击的IP数据包模式。作者已经提出了PCTT和2Dto2D可视化的组合使用,用于互联网的入侵检测。本文还介绍了作为PCTT新特性之一的样条平行坐标表示。作者还提出利用PCTT进行学习分析,将学习者的学习活动数据可视化,并在PCTT中引入3D模式,使每个学习者的学习模式更有效地可视化。这种三维模式被认为是三维平行坐标。然而,由于这种3D模式不足以区分每个学习者的学习模式,作者实现了更有效的3D模式,并通过显示可视化结果来阐明新的3D模式的有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time-Tunnel: 3D Visualization Tool and Its Aspects as 3D Parallel Coordinates
This paper treats a 3D visualization tool called Time-tunnel, especially describes its aspects as 3D Parallel Coordinates by showing its actual visualization examples. Originally, Time-tunnel is a multidimensional data visualization tool and it was extended to support Parallel Coordinates called PCTT(Parallel Coordinates version of Time-tunnel). Furthermore, as its aspects of 3D Parallel Coordinates, 2Dto2D visualization functionality was added. Although PCTT can visualize network data because IP packets consist of many attributes and such multiple-attributes data can be visualized using Parallel Coordinates, 2Dto2D visualization functionality can more effectively visualize patterns of IP packets that seem network attacks. The authors have already proposed the combinatorial use of PCTT and 2Dto2D visualization for the intrusion detection of the Internet. This paper also introduces Spline Parallel Coordinates representation as one of the new features of PCTT. The authors also proposed the use of PCTT for learning analytics by visualizing leaners' learning activity data and introduced 3D mode into PCTT to visualize each learner's learning pattern more efficiently. This 3D mode is regarded as 3D Parallel Coordinates. However, because such 3D mode was not enough to distinguish each learner's leaning pattern, the authors implemented more effective 3D mode, and clarify the usefulness of the new 3D mode by showing visualization results.
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