Visualizing big data with augmented and virtual reality: Challenges and research agenda
Open accessAbstract
This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.
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Organisations are increasingly making use of data visualisation tools and methods to make sense of their big data structures.
“Organisations are increasingly making use of data visualisation tools and methods and to make sense of their big data structures.”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Olshannikova et al. 2015
In scenarios where the limitations of human perception and cognition are taken into account, greater levels of understanding and interpretation can be gained from the analysis and presentation of data using AI technologies.
“In scenarios where the limitations of human perception and cognition are taken into account, greater levels of understanding and interpretation can be gained from the analysis and presentation of data using AI technologies (Olshannikova et al. 2015).”
From Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2021) · cites this paper as Olshannikova et al. 2015
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Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · see citing works
“In scenarios where the limitations of human perception and cognition are taken into account, greater levels of understanding and interpretation can be gained from the analysis and presentation of data using AI technologies (Olshannikova, Ometov, Koucheryavy, & Olsson, 2015).”
BackgroundWarke, V., Kumar, S., Bongale, A., & Kotecha, K. (2021) · Sustainability · doi:10.3390/su131810139 · Cited by 103 · see citing works
- Authors
- Olshannikova, E., Ometov, A., Koucheryavy, Y., & Olsson, T.
- Affiliations
- Tampere University of Technology (FI)
- Venue
- Journal of Big Data
- Published
- 2015-10-01
- Volume
- 2
- Issue
- 1
- ISSN
- 2196-1115 (ISSN-L)
- OpenAlex
- W1961379460
- Cited by
- 315
- References
- 3
- Citations by year
- 2026: 9 · 2025: 26 · 2024: 36 · 2023: 26 · 2022: 30 · 2021: 38 · 2020: 34 · 2019: 37 · 2018: 41 · 2017: 25 · 2016: 12 · 2015: 1
- FWCI
- 22.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal Of Big Data (publishedVersion, cc-by)
- Tampere University Institutional Repository (Tampere University) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Keywords
- big data visualization, augmented reality, virtual reality, mixed reality, visual field, data classification, visualization tools, virtual reality display, augmented reality headsets
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