Immersive and collaborative data visualization using virtual reality platforms (original) (raw)
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Journal of Big Data
Typically, visual analytics has been deployed when data problems are ill defined and/or the configuration of the data is not easily subject to algorithmic analysis. Within the context of big data these characteristics are the norm, leading to increased interest in applications known as visual data mining (VDM). The aim of VDM is to augment algorithmic analysis with human visual cognition, where data variables are mapped to graphic attributes and differentiated through spatial position, shape and colour, thus bringing human visual perception and creativity to analysis [1, 2]. VDM utilizes graphic mapping techniques ranging from graphs and scatterplots to tree maps, display icons, tag clouds and cluster grams. While there has being some activity and speculation on the potential of virtual reality
Involve Me and I Will Understand!–Abstract Data Visualization in Immersive Environments
Lecture Notes in Computer Science, 2011
Literature concerning the visualization of abstract data in immersive environments is sparse. This publication is intended to (1) stimulate the application of abstract data visualization in such environments and to (2) introduce novel concepts involving the user as an active part of the interactive exploratory visualization process. To motivate discussion, requirements for the visualization of abstract data are reviewed and then related to the properties of immersive environments in order to show its potential for data visualization. This leads to the introduction of a novel concept for immersive visualization based on the involvement of the viewers into the data display. The usefulness of the concept is shown by two examples demonstrating that immersive environments are far more than tools to create visually appealing data representations. 1 Introduction Visualization has been quite successful for data analysis, but limitations still exist. One limiting factor is the widely used two-dimensional (2D) display and interaction technology. Although we live and interact in a three-dimensional (3D) environment, most data visualizations use only two dimensions and neglect depth. This is often reasonable as the display of 3D data on 2D desktop screens imposes many drawbacks, such as occlusion, cumbersome interaction, and missing depth cues. First attempts to overcome these problems in data visualization were made by presenting data in immersive environments (IE). IEs are able to provide a synthetic 3D display and interaction space, which is rendered in the rst-person viewpoint [1]. Due to the use of stereoscopic-vision and motion parallax, IEs are able to mimic our natural 3D viewing environment and thus can provide a high level of physical immersion (see Figure 1, left). Early IEs relied on costly hardware, but with the recent boost in 3D display hardware, the technology now becomes much more aordable. Research to visualization in IES mainly focusses on spatial data. Not much is known about abstract data. This might be due to the fact that the natural representation of available spatial components is often considered to be the The author gratefully acknowledges the support of Deutsche Forschungsgemeinschaft (DFG) for funding this research (#RO3755/1-1) Compared to the immersive visualization of spatial data, often referred to as scientic data, not much is known about iAV. Existing approaches can be mainly subdivided into two groups-strategies with or without using the virtual world metaphor. Approaches applying the virtual world metaphor use visuals that mimic principles and behavior of the real world. Most strategies create a virtual world
Visualizing Large, Heterogeneous Data in Hybrid-Reality Environments
2013
V ision is our dominant sense, with roughly a quarter of our brain devoted to processing visual stimuli, providing the highestbandwidth perceptual channel into our cognitive systems. This advanced neural circuitry, shaped by millions of years of evolution, lets us recognize recurring patterns, quickly attend to the unexpected, and visually solve complex spatial and abstract problems. It's no surprise that early optical instruments were central to many scientific breakthroughs, from Anton van Leeuwenhoek's discovery of living cells in 1674 using a self-built microscope to Pierre Janssen and Norman Lockyer's discovery of helium in 1868 via direct observation of solar prominences with a telescope. Much of the data scientists investigate today is digitally created, stored, and analyzed. Scientists are observing phenomena with new types of digital instruments, sensors, and robotic exploration vehicles that can collect data at ever-increasing resolutions. Although this trove of modern digital data isn't directly amenable to visual observation with traditional optical lenses, visualization lets us transform this data into visual representations.
2000
There exist a number of scientific visualization systems designed to provide a two-dimensional interface to the user. However, little consideration has been given to the development of collaborative virtual environments for visualization purposes. This paper discusses the Tele-Immersive Data Explorer a generalizable framework to facilitate the construction of d omain-specific data exploration applications challenged with the problem of having to visualize massive data-sets immersively and collaboratively. In the paper we describe the framework's conceptual organization, its distributed multiprocessed objectoriented architecture, and its application to visualize gridded scalar data.
The Data Visualisation and Immersive Analytics Research Lab at Monash University
Visual Informatics, 2020
The lab has been influential with contributions in algorithms, interaction techniques and experimental results in Network Visualisation, Interactive Optimisation and Geographic and Cartographic visualisation. It has also been a leader in the emerging topic of Immersive Analytics, which explores natural interactions and immersive display technologies in support of data analytics. We reflect on advances in these areas but also sketch our vision for future research and developments in data visualisation more broadly.
Explorative and dynamic visualization of data in virtual reality
Computational Statistics, 2004
S u m m a r y A software system has been developed for the study of dynamic glyph visualizations in the context of Visual Data Mining in Virtual Reality. The system uses parallel processing to calculate data visualizations in real-time, with real-time interaction and dynamic changes to the view. The system allows morphing between different visualizations, the use of dynamic features like "vibrations" and "rotations" of thousands of objects individually, and dynamic visualization, where the influence of any variable of a dataset with a "reasonable" distribution, can be shown as a dynamic development. It appears that these facilities for dynamic data visualization have a very promising potential, but their optimal use will depend on further developments in the context of their individual practical application.
Virtual Reality Spaces: Visual Data Mining with a Hybrid Computational Intelligence Tool
The information explosion requires the development of alternative data mining procedures that speed up the process of scientific discovery. The improved in-depth understanding and ease of interpretability of the internal structure of data by investigators allows focussing on the most important issues, which is crucial for the identification of valid, novel, potentially useful, and understandable patterns (regularities, oddities, surprises, etc). Computational visualization techniques are used to explore, in an immersive fashion, inherent data structure in both an unsupervised and supervised manner. Supervision is provided via i) domain knowledge contained in the data, and ii) unsupervised data mining procedures, such as fuzzy clustering, etc. The Virtual Reality (VR) approach for large heterogeneous, incomplete and imprecise (fuzzy) information is introduced for the problem of visualizing and analyzing general forms of data. The method is based on mappings between a heterogeneous space representing the data, and a homogeneous virtual reality space. This VR-based visual data mining technique allows the incorporation of the unmatched geometric capabilities of the human brain into the knowledge discovery process. Traditional means of interpretation would require more time and effort in order to achieve the same level of deep understanding of complex high dimensional data as the proposed technique. This hybrid approach has been applied successfully to a wide variety of real-world domains including astronomy, genomics, and geology, providing useful insights.
High Dimensional Data Visualization: Advances and Challenges
International Journal of Computer Applications, 2017
Recent technological advances and availability of computing resources resulted in a massive growth of data size, dimensions and complexity. Data visualization is a good approach when dealing with large scale high dimensional datasets as it will provide the opportunity to understand what's in the data and where to focus. However, the ever increasing dimensions of datasets, the physical limitations of the display screen (2D/3D), and the relatively small capacity of our mind to process complex data at a time pose a challenge in the process of visualization. This paper describe the advancements made so far in visualizing high dimensional data and the challenges that should be addressed in future researches.
Large-scale comparative visualisation of sets of multidimensional data
PeerJ Computer Science, 2016
We presentencube—a qualitative, quantitative and comparative visualisation and analysis system, with application to high-resolution, immersive three-dimensional environments and desktop displays.encubeextends previous comparative visualisation systems by considering: (1) the integration of comparative visualisation and analysis into a unified system; (2) the documentation of the discovery process; and (3) an approach that enables scientists to continue the research process once back at their desktop. Our solution enables tablets, smartphones or laptops to be used as interaction units for manipulating, organising, and querying data. We highlight the modularity ofencube, allowing additional functionalities to be included as required. Additionally, our approach supports a high level of collaboration within the physical environment. We show how our implementation ofencubeoperates in a large-scale, hybrid visualisation and supercomputing environment using the CAVE2 at Monash University, ...