The document proposes developing an immersive visual analytics platform using virtual reality. It aims to address challenges from increasing data complexity and production by combining automated analysis and interactive visualization. The proposed platform would use Unity for visualization and Python for data analysis. This would allow researchers to focus on evaluating VR's potential to improve the knowledge discovery process compared to conventional 2D tools. Current progress includes a prototype connecting Unity and Python via gRPC for data exchange, and experiments with different interaction methods. Next steps involve improving data exchange, usability testing, and integrating VR hardware.

1. Improved Knowledge from Data: Building
an Immersive Data Analysis Platform
Dept. of Computer Engineering and Industrial Automation (DCA) - School of Electrical and Computer
Engineering - University of Campinas (UNICAMP)
Felipe Augusto Pedroso Paula Dornhofer Paro Costa

2. Visual Analytics (VA)
The production of data is achieving unprecedented levels accompanied by a
dramatic increase of its complexity.
The VA field tries to address the challenges imposed by this growth by
combining automated analysis techniques with interactive visualizations.
Its techniques and practices are well established for: emergency
management, monitoring climate and weather, scientific applications,
business intelligence, etc.

3. Visual Analytics (VA)
The production of data is achieving unprecedented levels accompanied by a
dramatic increase of its complexity.
The VA field tries to address the challenges imposed by this growth by
combining automated analysis techniques with interactive visualizations.
Its techniques and practices are well established for: emergency
management, monitoring climate and weather, scientific applications,
business intelligence, etc.

4. Visual Analytics (VA)
The production of data is achieving unprecedented levels accompanied by a
dramatic increase of its complexity.
The VA field tries to address the challenges imposed by this growth by
combining automated analysis techniques with interactive visualizations.
Its techniques and practices are well established for: emergency management,
monitoring climate and weather, scientific applications, business intelligence,
etc.

5. VA Analytics Process
The VA process proposed by Keim et al.

6. VA using Virtual Reality (VR)
The VR technologies are becoming affordable to be used by VA researchers,
allowing them to create visualizations that take advantage of the immersion
and user experience found in VR.
Immersive Analytics is a derivative field from VA and it aims to explore the
usage of immersive technologies to improve the way we extract knowledge
from data.

7. VA using Virtual Reality (VR)
The VR technologies are becoming affordable to be used by VA researchers,
allowing them to create visualizations that take advantage of the immersion
and user experience found in VR.
Immersive Analytics is a derivative field from VA and it aims to explore the
usage of immersive technologies to improve the way we extract knowledge
from data.

8. VA using VR: Current Research
The perceptions of using VR to visualize data range from positive results [7] to
others pointing that the conventional 2D visualization presents better results
over an immersive solution.
The authors didn't use any standard tool to create the visualizations used
during their studies, always implementing their solutions from scratch.

9. VA using VR: Current Research
The perceptions of using VR to visualize data range from positive results [7] to
others pointing that the conventional 2D visualization presents better results
over an immersive solution.
The authors didn't use any standard tool to create the visualizations used
during their studies, always implementing their solutions from scratch.

10. Justification and Relevance
Since everyone is creating their solutions from scratch, the visualizations could
have biases related to their implementation context and its limitations.
Using a common platform could enable researchers to:
● Focus on the evaluation step;
● Explore new VA use cases of VR technology;
● Help the field to establish VR as a tool to extract knowledge from big data.

11. Justification and Relevance
Since everyone is creating their solutions from scratch, the visualizations could
have biases related to their implementation context and its limitations.
Using a common platform could enable researchers to:
● Focus on the evaluation step;
● Explore new VA use cases of VR technology;
● Help the field to establish VR as a tool to extract knowledge from big data.

12. Hypothesis
IF Virtual Reality (VR) can provide a better user experience
and immersion
THEN the Visual Analytics process could benefit from it,
using VR characteristics to improve the user interaction
and make the knowledge discovery more effective.

13. Hypothesis
IF Virtual Reality (VR) can provide a better user experience
and immersion
THEN the Visual Analytics (VA) process could benefit from
it, using VR characteristics to improve the user interaction
and make the knowledge discovery more effective.

14. Research Goal
Implement an Immersive VA platform that
enables researchers to use and evaluate if VR
characteristics could improve the data analysis
and knowledge discovery processes

15. Proposed Solution
Develop an Immersive VA platform using VR to allow the evaluation of the
technology as part of the VA process.
The platform will be built with an open, free and extensible foundation.
We are evaluating the usage of Unity to implement the visualization and
Python to handle the data analysis step.

16. Proposed Solution
Develop an Immersive VA platform using VR to allow the evaluation of the
technology as part of the VA process.
The platform will be built with an open, free and extensible foundation.
We are evaluating the usage of Unity to implement the visualization and
Python to handle the data analysis step.

17. Proposed Solution
Develop an Immersive VA platform using VR to allow the evaluation of the
technology as part of the VA process.
The platform will be built with an open, free and extensible foundation.
We are evaluating the usage of Unity to implement the visualization and
Python to handle the data analysis step.

18. Proposed Solution - Workflow

19. Methodology
1. Literature review around Virtual Reality and Visual Analytics
2. Technologies evaluation
3. Prototype development
4. First Experiment to evaluate platform usability
5. Adjustments and fine tuning
6. Second experiment to evaluate VR as VA Tools
7. Compilation of results and final report writing

20. Current Progress
The communication between Unity and Python is implemented using gRPC.
This approach has a nice performance but poses restrictions to the type of
data being transmitted (gRPC only supports structured data).
We are experimenting two ways to provide user interaction:
● An Unity VR app that uses Python as a “data analysis backend”
● A Jupyter Notebook integrated with an Unity WebVR app

21. Current Progress
The communication between Unity and Python is implemented using gRPC [1]
This approach has a nice performance but poses restrictions to the type of
data being transmitted (gRPC only supports structured data)
We are experimenting two ways to provide user interaction:
● An Unity VR app that uses Python as a “data analysis backend”
● A Jupyter Notebook integrated with an Unity WebVR app

22. Current Progress
The communication between Unity and Python is implemented using gRPC [1]
This approach has a nice performance but poses restrictions to the type of
data being transmitted (gRPC only supports structured data)
We are experimenting two ways to provide user interaction:
● An Unity VR app that uses Python as a “data analysis backend”
● A Jupyter Notebook integrated with an Unity WebVR app

23. Python + Unity: First Prototype

24. Python + Unity: Current Status

25. Next Steps
Evaluate other ways to exchange data between Unity and Python to support
unstructured data
Test the performance with bigger datasets
Integrate libraries or assets to use VR hardware
Elaborate an usability experiment using the process proposed by Lazar et al.
(2017)

26. Dept. of Computer Engineering and Industrial Automation (DCA) School of Electrical and Computer
Engineering - University of Campinas (UNICAMP)
Paula D. Paro Costa
paula@fee.unicamp.br
Felipe A. Pedroso
felipe.pedroso@live.com

27. Thanks!

28. References
[1] gRPC open-source universal RPC framework. https://grpc.io/. [Online; accessed 30-August-2018].
[2] T. Chandler, M. Cordeil, T. Czauderna, T. Dwyer, J. Glowacki, C. Goncu, M. Klapperstueck, K. Klein, K. Marriott, F. Schreiber, and E. Wilson.
Immersive analytics. In 2015 Big Data Visual Analytics (BDVA), pp. 1–8, Sept 2015. doi: 10.1109/BDVA.2015.7314296
[3] C. Donalek, S. G. Djorgovski, A. Cioc, A. Wang, J. Zhang, E. Lawler, S. Yeh, A. Mahabal, M. Graham, A. Drake, et al. Immersive and
collaborative data visualization using virtual reality platforms. In Big Data (Big Data), 2014 IEEE International Conference on,pp.609–614. IEEE,
2014.
[4] E. Y. Gorodov and V. V. Gubarev. Analytical review of data visualization methods in application to big data. JECE, 2013:22:2–22:2, Jan. 2013.
doi: 10.1155/2013/969458
[5] P. J. C. John R Harger. Comparison of open-source visual analytics toolkits. vol. 8294, pp. 8294 – 8294 – 10, 2012. doi: 10.1117/12. 911901

29. References
[6] D. Keim, J. Kohlhammer, and G. Ellis. Mastering the Information Age: Solving Problems with Visual Analytics. Eurographics Association, 1st
ed., 2010.
[7] O.Kwon,C.Muelder,K.Lee,andK.Ma. Astudyoflayout,rendering, and interaction methods for immersive graph visualization. IEEE
Transactions on Visualization and Computer Graphics, 22(7):1802– 1815, July 2016. doi: 10.1109/TVCG.2016.2520921
[8] B. Marr. Big data: 20 mind-boggling facts everyone must read. https://www.forbes.com/sites/bernardmarr/2015/09/30/
big-data-20-mind-boggling-facts-everyone-must-read, Nov 2015. [Online; accessed 30-August-2018].
[9] J. A. Wagner Filho, M. F. Rey, C. M. Freitas, and L. Nedel. Immersive analytics of dimensionally-reduced data scatterplots. In 2nd Workshop
on Immersive Analytics. IEEE, 2017.
[10] L.Zhang,A.Stoffel,M.Behrisch,S.Mittelstadt,T.Schreck,R.Pompl, S. Weber, H. Last, and D. Keim. Visual analytics for the big data eraa
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173–182. IEEE, 2012.