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Improved Knowledge from Data: Building an Immersive Data Analysis Platform

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Improved Knowledge from Data: Building an Immersive Data Analysis Platform

  1. 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. 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. 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. 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. 5. VA Analytics Process The VA process proposed by Keim et al.
  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 18. Proposed Solution - Workflow
  19. 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. 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. 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. 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. 23. Python + Unity: First Prototype
  24. 24. Python + Unity: Current Status
  25. 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. 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. 27. Thanks!
  28. 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. 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 comparative review of state-of-the-art commercial systems. In Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on, pp. 173–182. IEEE, 2012.

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