Collaborative immersive analytics

Mark Billinghurst, Maxime Cordeil, Anastasia Bezerianos, Todd Margolis

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

7 Citations (Scopus)

Abstract

Many of the problems being addressed by Immersive Analytics require groups of people to solve. This chapter introduces the concept of Collaborative Immersive Analytics (CIA) and reviews how immersive technologies can be combined with Visual Analytics to facilitate co-located and remote collaboration. We provide a definition of Collaborative Immersive Analytics and then an overview of the different types of possible collaboration. The chapter also discusses the various roles in collaborative systems, and how to support shared interaction with the data being presented. Finally, we summarize the opportunities for future research in this domain. The aim of the chapter is to provide enough of an introduction to CIA and key directions for future research, so that practitioners will be able to begin working in the field.

Original languageEnglish
Title of host publicationImmersive Analytics
EditorsKim Marriott, Falk Schreiber, Tim Dwyer, Karsten Klein, Nathalie Henry Riche, Takayuki Itoh, Wolfgang Stuerzlinger, Bruce H. Thomas
Place of PublicationCham Switzerland
PublisherSpringer
Chapter8
Pages221-257
Number of pages37
ISBN (Electronic)9783030013882
ISBN (Print)9783030013875
DOIs
Publication statusPublished - 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11190
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Collaboration
  • Collaborative Immersive Analytics
  • Collaborative visualization
  • Human-computer interaction
  • Immersion
  • Immersive Analytics
  • Shared interaction
  • Visual analytics

Cite this

Billinghurst, M., Cordeil, M., Bezerianos, A., & Margolis, T. (2018). Collaborative immersive analytics. In K. Marriott, F. Schreiber, T. Dwyer, K. Klein, N. Henry Riche, T. Itoh, W. Stuerzlinger, & B. H. Thomas (Eds.), Immersive Analytics (pp. 221-257). (Lecture Notes in Computer Science ; Vol. 11190 ). Springer. https://doi.org/10.1007/978-3-030-01388-2_8