Immersive and collaborative data visualization using virtual reality platforms
By C. Donalek, S. George Djorgovski, Scott Davidoff, Alexandru Cioc, An-Hsiang Wang, G. Longo, J. Norris, Jerry Zhang, E. Lawler, Stacy Yeh and 3 more. 2014 IEEE International Conference on Big Data (Big Data), 2014
366 citations recorded by the source corpus.
Effective data visualization is a key part of the discovery process in the era of “big data”. It is the bridge between the quantitative content of the data and human intuition, and thus an essential component of the scientific path from data into knowledge and understanding. Visualization is also essential in the data mining process, directing the choice of the applicable algorithms, and in helping to identify and remove bad data from the analysis. However, a high complexity or a high dimensionality of modern data sets represents a critical obstacle. How do we visualize interesting structures and patterns that may exist in hyper-dimensional data spaces? A better understanding of how we can perceive and interact with multidimensional information poses some deep questions in the field of cognition technology and human-computer interaction. To this effect, we are exploring the use of immersive virtual reality platforms for scientific data visualization, both as software and inexpensive commodity hardware. These potentially powerful and innovative tools for multi-dimensional data visualization can also provide an easy and natural path to a collaborative data visualization and exploration, where scientists can interact with their data and their colleagues in the same visual space. Immersion provides benefits beyond the traditional “desktop” visualization tools: it leads to a demonstrably better perception of a datascape geometry, more intuitive data understanding, and a better retention of the perceived relationships in the data.
Subfields: Computer Vision and Pattern Recognition, Social Psychology, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/6979c7226b09b6083ece0a0c0bbca83192541e57
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Citations in the corpus
- Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges (2017, background): “Similarly, when fine-grained dimensions are visualized effectively, the probability to identify observable correlations, patterns, and outliners is high [89].”
- Internet of Things and Big Data as enablers for business digitalization strategies (2020, background): “When selecting technologies to redesign their internal processes, companies should involve staff at all levels and focus on new ways of working (Donalek et al., 2014; Peng et al., 2018).”