Glyphs representing complex behavior provide a useful and common means of visualizing multivariate data. However, due to their complex shape, overlapping, and occlusion of glyphs is a common and prominent limitation. This limits the number of discreet data tuples that can be displayed in a given image. Using a real-world application, glyphs are used to depict agent behavior in a call center. However, many call centers feature thousands of agents. A standard approach representing thousands of agents with glyphs does not scale. To accommodate the visualization incorporating thousands of glyphs we develop clustering of overlapping glyphs into a single parent glyph. This hierarchical glyph represents the mean value of all child agent glyphs, removing overlap and reduTcing visual clutter. Multi-variate clustering techniques are explored and developed in collaboration with domain experts in the call center industry. We implement dynamic control of glyph clusters according to zoom level and customized distance metrics, to utilize image space with reduced overplotting and cluttering. We demonstrate our technique with examples and a usage scenario using real-world call-center data to visualize thousands of call center agents, revealing insight into their behavior and reporting feedback from expert call-center analysts.

中文翻译：

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AgentVis：使用分层字形对代理行为进行可视化分析

表示复杂行为的字形提供了一种有用且通用的可视化多元数据的方法。然而，由于它们的复杂形状，字形的重叠和遮挡是一个常见且突出的限制。这限制了可以在给定图像中显示的离散数据元组的数量。使用真实世界的应用程序，字形用于描述呼叫中心中的座席行为。然而，许多呼叫中心都有成千上万的座席。代表数千个具有字形的代理的标准方法无法扩展。为了适应包含数千个字形的可视化，我们将重叠的字形聚类到单个父字形中。这个分层字形代表所有子代理字形的平均值，去除重叠并减少视觉混乱。与呼叫中心行业的领域专家合作探索和开发了多变量聚类技术。我们根据缩放级别和自定义距离指标实现字形集群的动态控制，以利用图像空间减少过度绘制和混乱。我们通过示例和使用场景展示了我们的技术，使用真实世界的呼叫中心数据来可视化数千个呼叫中心座席，揭示他们的行为并报告专家呼叫中心分析师的反馈。