Immersive Insights: A Hybrid Analytics System for Collaborative Exploratory Data Analysis
Marco Cavallo, Mishal Dholakia, Matous Havlena, Kenneth Ocheltree,, Mark Podlaseck

TL;DR
This paper introduces Immersive Insights, a hybrid AR/VR analytics system designed for collaborative exploratory data analysis, and evaluates its effectiveness through user studies comparing different immersion levels against traditional methods.
Contribution
It presents a novel hybrid analytics system combining AR, VR, and high-resolution displays for collaborative EDA, and provides empirical insights into the benefits of immersion in data analysis workflows.
Findings
Immersive environments improved collaboration among data scientists.
Higher immersion levels enhanced data exploration efficiency.
Immersive Insights outperformed non-immersive systems in certain tasks.
Abstract
In the past few years, augmented reality (AR) and virtual reality (VR) technologies have experienced terrific improvements in both accessibility and hardware capabilities, encouraging the application of these devices across various domains. While researchers have demonstrated the possible advantages of AR and VR for certain data science tasks, it is still unclear how these technologies would perform in the context of exploratory data analysis (EDA) at large. In particular, we believe it is important to better understand which level of immersion EDA would concretely benefit from, and to quantify the contribution of AR and VR with respect to standard analysis workflows. In this work, we leverage a Dataspace reconfigurable hybrid reality environment to study how data scientists might perform EDA in a co-located, collaborative context. Specifically, we propose the design and…
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