English

Immersive and Collaborative Data Visualization Using Virtual Reality Platforms

Human-Computer Interaction 2016-11-18 v1 Instrumentation and Methods for Astrophysics

Abstract

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 multi dimensional 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.

Keywords

Cite

@article{arxiv.1410.7670,
  title  = {Immersive and Collaborative Data Visualization Using Virtual Reality Platforms},
  author = {Ciro Donalek and S. G. Djorgovski and Scott Davidoff and Alex Cioc and Anwell Wang and Giuseppe Longo and Jeffrey S. Norris and Jerry Zhang and Elizabeth Lawler and Stacy Yeh and Ashish Mahabal and Matthew Graham and Andrew Drake},
  journal= {arXiv preprint arXiv:1410.7670},
  year   = {2016}
}

Comments

6 pages, refereed proceedings of 2014 IEEE International Conference on Big Data, page 609, ISBN 978-1-4799-5665-4

R2 v1 2026-06-22T06:38:52.561Z