本科生毕业论文(设计)
题 目 The affordance of virtual reality to enable the sensory representation of multi-dimensional data for immersive analytics:from experience to insight
学生姓名 陈苏艺
学 号 20151396043
学 院 传媒与艺术
专 业 艺术与科技
指导教师 李凤麒
二O一九年四月二十四日
The affordance of virtual reality to enable the sensory representation of multi-dimensional data for immersive analytics:from experience to insight
Author
Authors and affiliations
- Jules Moloney
- Branka Spehar
- Anastasia Globa
- Rui Wang
Abstract: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.
Keywords: Virtual reality Affordance Big data Multisensory representation Human interaction
1 Introduction
Typically, visual analytics has been deployed when data problems are ill defined and/or the configuration of the data is not easily subject to algorithmic analysis. Within the context of big data these characteristics are the norm, leading to increased interest in applications known as visual data mining (VDM). The aim of VDM is to augment algorithmic analysis with human visual cognition, where data variables are mapped to graphic attributes and differentiated through spatial position, shape and colour, thus bringing human visual perception and creativity to analysis [1, 2]. VDM utilizes graphic mapping techniques ranging from graphs and scatterplots to tree maps, display icons, tag clouds and cluster grams. While there has being some activity and speculation on the potential of virtual reality (VR) for visual analytics, application has been hindered by limited access to suitable hardware [3, 4, 5]. The recent availability of low cost, high performing head mounted displays (HMD) and compatibility with feature rich application authoring platforms such as Unity, has facilitated increased interest in the potential of VR for visual analytics. This has led to the new sub-field of research termed immersive analytics, which explores the potential of immersion in VR to extend existing graphic mapping techniques [6].
The position we develop in this article contributes to the research agenda of immersive analytics through considering the affordanceof the technology, with the objective of developing key principles that can inform the specification of software prototypes for immersive analytics. The term affordance will be familiar to many readers through Hartson [7], who clarified the practical utility of the theory for the design of Human Computer Interfaces (HCI). The concept of affordance was originally developed in psychology by James J. Gibson, whose lsquo;Ecological Approach to Visual Perceptionrsquo; [8] was described at the time as a revolutionary shift away from cognitive theories of psychology [9]. As we will discuss, there are a range of ways in which affordance has been defined and used in different domains, but for the purposes of this introduction the broad definition below by Stuckey et al. developed in relation to the design of virtual environments, captures our intent. “hellip; we use the concept of affordance to refer to the latent possibilities for action presented by an artefact, tool or environment” [ 剩余内容已隐藏,支付完成后下载完整资料
题 目 虚拟现实的可提供性,使多维数据的感官表征为沉浸式分析:从经验到洞察
虚拟现实的可提供性,使多维数据的感官表征为沉浸式分析:从经验到洞察
法蒂玛·扎赫拉·卡哈特、塞西尔·勒·普拉多、阿雷蒂·达马拉和皮埃尔·库波特
{fatima.azough, leprado, cubaud} @cnam.fr, areti.damala@gmail.com
1介绍
通常, 当数据问题定义不明确且数据配置不容易受到算法分析时, 就会部署可视化分析。在大数据的背景下, 这些特征是常态, 导致人们对被称为可视化数据挖掘 (VDM) 的应用程序的兴趣增加。VDM 的目的是利用人类的视觉认知来增强算法分析, 将数据变量映射到图形属性, 并通过空间位置、形状和颜色进行区分, 从而将人的视觉感知和创造力引入分析 [1, 2]。VDM 采用图形映射技术, 从图形和散点图到树图、显示图标、标记云和聚类图。虽然对虚拟现实 (VR) 在可视化分析方面的潜力有一些活动和猜测, 但由于对合适硬件的访问有限, 应用程序受到阻碍 [3, 4, 5]。最近提供了低成本、高性能的头戴式显示器 (HMD), 并与 Unity 等功能丰富的应用程序创作平台兼容, 这促进了人们对 VR 可视化分析潜力的兴趣增加。这导致了被称为沉浸式分析的新的研究子领域, 探索了沉浸在虚拟现实中扩展现有图形映射技术的潜力 [6]。
我们在本文中所处的位置是通过考虑该技术的可提供性,从而为沉浸式分析的研究议程做出贡献,其目标是开发能够为沉浸式分析的软件原型规范提供信息的关键原则。通过Hartson[7],许多读者将熟悉可提供性这个术语,他阐明了该理论在人机界面(HCI)设计中的实际应用。启示的概念最初是由James J. Gibson在心理学中提出的,他的“视觉感知的生态方法”[8]在当时被描述为对心理学[9]认知理论的革命性转变。正如我们将要讨论的,在不同的领域中,有许多方法定义和使用了可视性,但是为了介绍的目的,Stuckey等人在下面开发的与虚拟环境设计相关的广义定义捕获了我们的意图。“hellip;hellip;我们使用可提供性的概念来指由人工制品、工具或环境所呈现的潜在行动可能性”[10]。
为了解决这个问题,本文分为两部分。第一项研究对VDM和VR实验进行了有针对性的文献综述,这些实验都使用了带有运动和声音的增强视觉映射。然后我们介绍了新兴的沉浸式分析领域,这是一个由研究人员组成的网络,他们正在探索VR在数据表示方面的潜力。通过对沉浸式分析的三篇观点论文的批判性讨论,我们阐明了从以分配为中心的视觉分析向以自我为中心的空间编码的转变。在神经科学研究中,已经发现这两种编码分别与视觉智能和空间智能[11]保持一致。第一部分的目标是从事相关的以前的工作和当前的想法,以便将本文的范在最后的讨论中,我们整理了整篇文章的观点,通过一个图表,总结了VR与在计算机显示器上进行的可视化数据挖掘相比的可用性。然后,从启示理论的角度,提出了开发沉浸式分析应用的关键原则。支撑这些原则的核心思想是,通过指定与人类生态感知能力相关的信息密集的虚拟环境,有机会开发新的方法来探索大数据中的模式、异常和联系。围定位在更广泛的数据分析和混合现实领域。
2可视化数据探索与VR1.0
3下一代虚拟现实和沉浸式分析
4沉浸式分析的本体
在我们的文献综述中,Olshannikova et al.[19]、Chandler et al.[6]和Sadana et al.最近发表的三篇立场论文综述了关键挑战和研究问题,如表1所示。
Olshannikova等人的[19]、Chandler等人的[6]和Sadana等人的立场论文中关于沉浸式分析的挑战和研究问题综述。
4.1可得性理论的起源及HCI设计的适应性
4.2 Mimetics、启示和虚拟环境
4.3跨模态映射
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