Using the theory of affordance from perceptual psychology and through discussion of literature within visual data mining and immersive analytics, a position for the multi-sensory representation of big data using virtual reality (VR) is developed. While it would seem counter intuitive, information-dense virtual environments are theoretically easier to process than simplified graphic encoding—if there is alignment with human ecological perception of natural environments. Potentially, VR affords insight into patterns and anomalies through dynamic experience of data representations within interactive, kinaesthetic audio-visual virtual environments. To this end we articulate principles that can inform the development of VR applications for immersive analytics: a mimetic approach to data mapping that aligns spatial, aural and kinaesthetic attributes with abstractions of natural environments; layered with constructed features that complement natural structures; the use of cross-modal sensory mapping; a focus on intermediate levels of contrast; and the adaptation of naturally occurring distribution patterns for the granularity and distribution of data. While it appears problematic to directly translate visual data mining techniques to VR, the ecological approach to human perception discussed in this article provides a new framework for big data visualization researchers to consider.

中文翻译：

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虚拟现实的提供使身临其境的分析能够对多维数据进行感官表示：从经验到见解

利用来自感知心理学的负担能力理论，并通过在视觉数据挖掘和沉浸式分析中对文献的讨论，开发了使用虚拟现实（VR）进行大数据的多传感器表示的位置。尽管看起来与直觉相反，但如果与人类对自然环境的生态感知保持一致，则信息密集型虚拟环境在理论上将比简化的图形编码更易于处理。VR可能通过在互动的，动觉的视听虚拟环境中动态呈现数据表示，从而洞察模式和异常。为此，我们阐述了一些原则，这些原则可以为沉浸式分析的VR应用程序开发提供信息：一种模拟的数据映射方法，可以将空间，具有自然环境抽象的听觉和动觉特性；具有与自然结构互补的构造特征分层；使用交叉模式的感觉映射；着重于中间对比水平；以及自然分布模式对数据粒度和分布的适应。虽然将可视数据挖掘技术直接转换为VR似乎存在问题，但本文讨论的生态感知人类方法为大数据可视化研究人员提供了一个新的框架。以及自然分布模式对数据粒度和分布的适应。虽然将可视数据挖掘技术直接转换为VR似乎存在问题，但本文讨论的生态感知人类方法为大数据可视化研究人员提供了一个新的框架。以及自然分布模式对数据粒度和分布的适应。虽然将可视数据挖掘技术直接转换为VR似乎存在问题，但本文讨论的生态感知人类方法为大数据可视化研究人员提供了一个新的框架。