Collaborative Immersive Analytics

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.

“Oh, that’s where you are!” – Towards a Hybrid Asymmetric Collaborative Immersive Analytics System

“…Thus, many important collaborative information cues (facial expressions, body language, spatial references, and more) are no longer available. However, enabling multiple users to collaboratively explore and interpret data is often desired: (1) the analysis of large datasets requires a broad expertise, unfeasible to be covered by a single analyst [28,56]; (2) collaboration is more effective than working alone [6], arguably because it is anchored within the human nature [42]; (3) besides perceptual and cognitive processes, visual analysis and decision making also involves social processes, such as analysts debating about the interpretation of data, providing individual and contextual knowledge [6,26]. Consequently, more research is needed to address such multi-disciplinary challenges in the area of collaborative IA to bridge the gap between user-centered experiences and collaborative data analysis.…”

“…Immersion has the potential to facilitate the exploration of data (e.g., depth cues as additional information dimension, presentation of data with spatial embedding, literally more space to arrange views, increased user engagement), prompting IA researchers to reconsider the value of 3D visualizations, since their application for data exploration is rather rare outside of Scientific Visualization (SciVis) [40]. Besides the actual visualization and interaction with data in the 3D space [11], another important aspect determining the success of IA is concerned with the collaborative capabilities of an IA system [6]. However, wearing a HMD to immersive oneself in the VR environment (visually) isolates its user from the physical surroundings.…”

“…Within the scope of collaborative IA, insights from the area of CSCW can be informative, researching how computer systems can support collaborative activities conducted by more than one user. More specifically in this context, definitions for Collaborative Visualization [28], User-Centered Collaborative Visualization [12], and Collaborative IA [6] exist.…”

“…First, many studies focused around collaborative IA assume all collaborators use immersive technologies. Second, few studies to date have explored (remote) collaboration, as well as effects of using asymmetric technologies (for instance awareness, deixis, or group dynamics) within the context of IA [6]. Third, we present an applied real-world case study of IA within the context of the Digital Humanities.…”

“…Having a lookup table of each place and the cluster it belongs to, enables us to allocate and aggregate the tweets that were published from each place and also represent the place clusters using their center latitude and longitude. 6…”