9,461 research outputs found
Improving Big Data Visual Analytics with Interactive Virtual Reality
For decades, the growth and volume of digital data collection has made it
challenging to digest large volumes of information and extract underlying
structure. Coined 'Big Data', massive amounts of information has quite often
been gathered inconsistently (e.g from many sources, of various forms, at
different rates, etc.). These factors impede the practices of not only
processing data, but also analyzing and displaying it in an efficient manner to
the user. Many efforts have been completed in the data mining and visual
analytics community to create effective ways to further improve analysis and
achieve the knowledge desired for better understanding. Our approach for
improved big data visual analytics is two-fold, focusing on both visualization
and interaction. Given geo-tagged information, we are exploring the benefits of
visualizing datasets in the original geospatial domain by utilizing a virtual
reality platform. After running proven analytics on the data, we intend to
represent the information in a more realistic 3D setting, where analysts can
achieve an enhanced situational awareness and rely on familiar perceptions to
draw in-depth conclusions on the dataset. In addition, developing a
human-computer interface that responds to natural user actions and inputs
creates a more intuitive environment. Tasks can be performed to manipulate the
dataset and allow users to dive deeper upon request, adhering to desired
demands and intentions. Due to the volume and popularity of social media, we
developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing
emerging technologies of today to create a fully immersive tool that promotes
visualization and interaction can help ease the process of understanding and
representing big data.Comment: 6 pages, 8 figures, 2015 IEEE High Performance Extreme Computing
Conference (HPEC '15); corrected typo
TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy
We introduce TempoCave, a novel visualization application for analyzing
dynamic brain networks, or connectomes. TempoCave provides a range of
functionality to explore metrics related to the activity patterns and modular
affiliations of different regions in the brain. These patterns are calculated
by processing raw data retrieved functional magnetic resonance imaging (fMRI)
scans, which creates a network of weighted edges between each brain region,
where the weight indicates how likely these regions are to activate
synchronously. In particular, we support the analysis needs of clinical
psychologists, who examine these modular affiliations and weighted edges and
their temporal dynamics, utilizing them to understand relationships between
neurological disorders and brain activity, which could have a significant
impact on the way in which patients are diagnosed and treated. We summarize the
core functionality of TempoCave, which supports a range of comparative tasks,
and runs both in a desktop mode and in an immersive mode. Furthermore, we
present a real-world use case that analyzes pre- and post-treatment connectome
datasets from 27 subjects in a clinical study investigating the use of
cognitive behavior therapy to treat major depression disorder, indicating that
TempoCave can provide new insight into the dynamic behavior of the human brain
Immersive Analytics of Large Dynamic Networks via Overview and Detail Navigation
Analysis of large dynamic networks is a thriving research field, typically
relying on 2D graph representations. The advent of affordable head mounted
displays however, sparked new interest in the potential of 3D visualization for
immersive network analytics. Nevertheless, most solutions do not scale well
with the number of nodes and edges and rely on conventional fly- or
walk-through navigation. In this paper, we present a novel approach for the
exploration of large dynamic graphs in virtual reality that interweaves two
navigation metaphors: overview exploration and immersive detail analysis. We
thereby use the potential of state-of-the-art VR headsets, coupled with a
web-based 3D rendering engine that supports heterogeneous input modalities to
enable ad-hoc immersive network analytics. We validate our approach through a
performance evaluation and a case study with experts analyzing a co-morbidity
network
The use of Virtual Reality in Enhancing Interdisciplinary Research and Education
Virtual Reality (VR) is increasingly being recognized for its educational
potential and as an effective way to convey new knowledge to people, it
supports interactive and collaborative activities. Affordable VR powered by
mobile technologies is opening a new world of opportunities that can transform
the ways in which we learn and engage with others. This paper reports our study
regarding the application of VR in stimulating interdisciplinary communication.
It investigates the promises of VR in interdisciplinary education and research.
The main contributions of this study are (i) literature review of theories of
learning underlying the justification of the use of VR systems in education,
(ii) taxonomy of the various types and implementations of VR systems and their
application in supporting education and research (iii) evaluation of
educational applications of VR from a broad range of disciplines, (iv)
investigation of how the learning process and learning outcomes are affected by
VR systems, and (v) comparative analysis of VR and traditional methods of
teaching in terms of quality of learning. This study seeks to inspire and
inform interdisciplinary researchers and learners about the ways in which VR
might support them and also VR software developers to push the limits of their
craft.Comment: 6 Page
Extending adjacency matrices to 3D with triangles
Social networks are the fabric of society and the subject of frequent visual
analysis. Closed triads represent triangular relationships between three people
in a social network and are significant for understanding inherent
interconnections and influence within the network. The most common methods for
representing social networks (node-link diagrams and adjacency matrices) are
not optimal for understanding triangles. We propose extending the adjacency
matrix form to 3D for better visualization of network triads. We design a 3D
matrix reordering technique and implement an immersive interactive system to
assist in visualizing and analyzing closed triads in social networks. A user
study and usage scenarios demonstrate that our method provides substantial
added value over node-link diagrams in improving the efficiency and accuracy of
manipulating and understanding the social network triads.Comment: 10 pages, 8 figures and 3 table
Analysis domain model for shared virtual environments
The field of shared virtual environments, which also
encompasses online games and social 3D environments, has a
system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model
A study of event traffic during the shared manipulation of objects within a collaborative virtual environment
Event management must balance consistency and responsiveness above the requirements of shared object interaction within a Collaborative Virtual Environment
(CVE) system. An understanding of the event traffic during collaborative tasks helps in the design of all aspects of a CVE system. The application, user activity, the display
interface, and the network resources, all play a part in determining the characteristics of event management.
Linked cubic displays lend themselves well to supporting natural social human communication between remote users. To allow users to communicate naturally and subconsciously, continuous and detailed tracking is necessary. This, however, is hard to balance with the real-time consistency constraints of general shared object interaction.
This paper aims to explain these issues through a detailed examination of event traffic produced by a typical CVE, using both immersive and desktop displays, while supporting a variety of collaborative activities. We analyze event traffic during a highly collaborative task requiring various forms of shared object manipulation, including the concurrent manipulation of a shared object. Event sources are categorized and the influence of the form of object sharing as well as the display device
interface are detailed. With the presented findings the paper wishes to aid the design of future systems
Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications
Social Virtual Reality based Learning Environments (VRLEs) such as vSocial
render instructional content in a three-dimensional immersive computer
experience for training youth with learning impediments. There are limited
prior works that explored attack vulnerability in VR technology, and hence
there is a need for systematic frameworks to quantify risks corresponding to
security, privacy, and safety (SPS) threats. The SPS threats can adversely
impact the educational user experience and hinder delivery of VRLE content. In
this paper, we propose a novel risk assessment framework that utilizes attack
trees to calculate a risk score for varied VRLE threats with rate and duration
of threats as inputs. We compare the impact of a well-constructed attack tree
with an adhoc attack tree to study the trade-offs between overheads in managing
attack trees, and the cost of risk mitigation when vulnerabilities are
identified. We use a vSocial VRLE testbed in a case study to showcase the
effectiveness of our framework and demonstrate how a suitable attack tree
formalism can result in a more safer, privacy-preserving and secure VRLE
system.Comment: Tp appear in the CCNC 2019 Conferenc
A Study of Mental Maps in Immersive Network Visualization
The visualization of a network influences the quality of the mental map that
the viewer develops to understand the network. In this study, we investigate
the effects of a 3D immersive visualization environment compared to a
traditional 2D desktop environment on the comprehension of a network's
structure. We compare the two visualization environments using three
tasks--interpreting network structure, memorizing a set of nodes, and
identifying the structural changes--commonly used for evaluating the quality of
a mental map in network visualization. The results show that participants were
able to interpret network structure more accurately when viewing the network in
an immersive environment, particularly for larger networks. However, we found
that 2D visualizations performed better than immersive visualization for tasks
that required spatial memory.Comment: IEEE Pacific Visualization Symposium 202
- …