• Building Immersive Data Visualizations for the Web

      Butcher, Peter; Ritsos, Panagiotis D.; University of Chester; Bangor University (IEEE Conference Publications, 2017-09)
      We present our early work on building prototype applications for Immersive Analytics using emerging standards-based web technologies for VR. For our preliminary investigations we visualize 3D bar charts that attempt to resemble recent physical visualizations built in the visualization community. We explore some of the challenges faced by developers in working with emerging VR tools for the web, and in building effective and informative immersive 3D visualizations.
    • VRIA: A Web-based Framework for Creating Immersive Analytics Experiences

      Butcher, Peter; John, Nigel W; Ritsos, Panagiotis D.; University of Chester and Bangor University (IEEE, 2020-01-09)
      We present<VRIA>, a Web-based framework for creating Immersive Analytics (IA) experiences in Virtual Reality.<VRIA>is built upon WebVR, A-Frame, React and D3.js, and offers a visualization creation workflow which enables users, of different levels of expertise, to rapidly develop Immersive Analytics experiences for the Web. The use of these open-standards Web-based technologies allows us to implement VR experiences in a browser and offers strong synergies with popular visualization libraries, through the HTMLDocument Object Model (DOM). This makes<VRIA>ubiquitous and platform-independent. Moreover, by using WebVR’s progressive enhancement, the experiences<VRIA>creates are accessible on a plethora of devices. We elaborate on our motivation for focusing on open-standards Web technologies, present the<VRIA>creation workflow and detail the underlying mechanics of our framework. We also report on techniques and optimizations necessary for implementing Immersive Analytics experiences on the Web, discuss scalability implications of our framework, and present a series of use case applications to demonstrate the various features of <VRIA>. Finally, we discuss current limitations of our framework, the lessons learned from its development, and outline further extensions.