Evaluating the Benefits of Explicit and Semi-Automated Clusters in Immersive Space to Think

Immersive technologies provide an unconstrained three-dimensional space to solve sensemaking tasks by enabling rich semantic interaction to engage with documents in the environment. Sensemaking tasks require the user to create a hypothesis out of raw data from a pile of documents However, isolating the relevant documents from the pile is a vital task. To do that, the user needs to interact with multiple documents at the same time. As the user goes through the documents, she makes several groups with similar documents closer to each other. These groups of documents eventually help the user to answer questions related to the task.

However, making the groups is a trivial task that requires a manual effort from the user. Automating this would save the user valuable time and enable her to focus on the high-level tasks of extracting insights from the documents. This raises several key questions; such as how does a user create a cluster of documents in the 3D space? How to visualize the 3D clusters? How can the user interact with the whole cluster instead of single documents? Does the automated approach improve the performance of the users in their sensemaking tasks?

In this study, we investigate the mechanisms of interacting with multiple documents in 3D space to answer these questions. First, we propose an algorithm that can dynamically create clusters with documents that are spatially similar. Second, we compare three different user interfaces to visually demonstrate the created cluster: 2.5D visualization, connecting link visualization, and color-labeling border technique. Third, for each of the visual feedback, we also consider the interaction techniques of selecting and manipulating clusters in the 3D environment. Finally, we propose a user study to compare the effectiveness of the manual vs. automated clustering technique.

Here’s a sneak peek of what we’ve been working on.

@conference{tahmidevaluating,

title = {Evaluating the Benefits of Explicit and Semi-Automated Clusters for Immersive Sensemaking},

author = {Ibrahim A Tahmid and Lee Lisle and Kylie Davidson and Chris North and Doug A Bowman},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9995165},

doi = {10.1109/ISMAR55827.2022.00064},

year  = {2022},

date = {2022-10-17},

urldate = {2022-10-17},

publisher = {International Symposium on Mixed and Augmented Reality},

abstract = {Immersive spaces have great potential to support analysts in complex sensemaking tasks, but the use of only manual interactions

for organizing data elements can become tedious. We analyzed

the user interactions to support cluster formation in an immersive

sensemaking system, and we designed a semi-automated cluster creation technique that determines the user’s intent to create a cluster

based on object proximity. We present the results of a user study

comparing this proximity-based technique with a manual clustering technique and a baseline immersive workspace with no explicit

clustering support. We found that semi-automated clustering was

faster and preferred, while manual clustering gave greater control to

users. These results provide support for the approach of adding intelligent semantic interactions to aid the users of immersive analytics

systems},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}