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Monsanto symposium


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Visual analytics and research pipeline

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Monsanto symposium

  1. 1. VISUAL ANALYTICS, VISUALIZATION TECHNOLOGY, AND THE RESEARCH PIPELINE JillianAurisano University of Illinois,Chicago ElectronicVisualization Lab
  2. 2. Preview  A bit about me  A bit about my research and my lab  Advanced, Collaborative Visualization Platforms  “wall” in MRC Bangalore and mini-wall coming to St. Louis  Visualization of biology data  A bit about my work with Monsanto
  3. 3. About me Visualization
  4. 4. Electronic Visualization Lab  Research Lab at the University of Illinois, Chicago  Founded in 1973  Interdisciplinary: Computer Science, Art, Communications, Geosciece, Medicine and Biology  Research in: advanced displays, visualization and visual analytics, high-speed networking, interaction and collaboration
  5. 5. EVL collaborations  Universities  Northwestern University  University of Chicago  University of Tokyo  National Labs  Argonne National Labs  Fermi National Accelerator Labs  International Consortia  GeoWall Consortium  Industry  General Motors Research and Development  Cinegrid / Disney  Monsanto
  6. 6. New challenges in science and biology  Modern data sets are  Big  High-throughput experimental techniques  Increased rate of data collection  Complex  Multivariate  Difficult to understand
  7. 7. New challenges in science and biology, cont.  Modern research is  Collaborative  Global  Need to share information and work together with researchers across the room and across the globe.  Data is distributed
  8. 8. Summary  Massive amounts of data that are hard to access, process, understand and share.  Raises questions:  What can be done to help researchers fully leverage their data sets to build knowledge?  What infrastructure needs to be in place to enable collaboration and information sharing?  How can large, complex and multi-variate data sets be placed in a context that promotes understanding?
  9. 9. EVL: Visualization and collaboration platforms are part of the solution
  10. 10. New challenges in science call for new class of scientific instruments  Advanced visualization instruments to visualize data, facilitate collaboration and enable the synthesis of results  Classical scientific instruments: microscope, telescope  Single user  Current digital and visualization technologies  Also single user  Modern scientific instruments need to be different
  11. 11. Visualization and collaboration platforms: Part of the solution EVL has pursued research in advanced displays, high-speed networking and collaboration platforms Create displays that serve as portals through which to access, view and share scientific data. Use displays as a ‘lens’ through which to focus digital information at high resolution.
  12. 12. Tiled-display wall and SAGE
  13. 13. Tiled-display wall and SAGE  Tiled configuration of 46 inch displays each with megapixel resolution  EVL: 6x3 46 inch monitors, 18 megapixels , Touch-interface  MRC Bangalore: 3x3 46inch monitors, 9 megapixels  Soon… Chesterfield: 2x2 46 inch monitors, 5.5 megapixels  Leverages high-performance computation and high-speed networks and runs the SAGE user interface
  14. 14. High-speed networks and cyber- infrastructure  EVL has 30 gigabits of networking, same as the internet backbone  High speed networks make it possible to communicate more quickly overseas than to a personal hard drive
  15. 15. Easy to share, possible to juxtapose lots of information  Every person with a laptop and SAGE pointer application can move images on the wall, share images, share movies, share their desktop  n desktops screens shared at once  n images shared in one view  n movies playing at once  A session in Bangalore can be streamed in real time to a wall session in Chesterfield  Distant users can use the same digital interface across the world
  16. 16. Easy to share, possible to juxtapose lots of information  Scientific tool enabling collaboration both locally and around the world  High degree of information juxtaposition
  17. 17. Many programs that visualize biology data  Gene-Network visualization  Visualization of Gene Expression  Genome visualization  Comparative Genomic Visualization With programs including Cytoscape, PathwayStudio, Gbrowse, Spotfire But… none of these programs are designed for a display like the wall.
  18. 18. From tiny canvas to mural
  19. 19. Large, high resolution display as an instrument for biology data visualization  Leverage the power of the wall and SAGE, and build a new class of visualization approaches that will serve as a large and advanced ‘lens’ through which the data can be viewed, focused and contextualized  Large, complex data sets might be visualized in new ways if designed for the wall
  20. 20. What is a ‘visualization’?  Visualization  Represent or encode data in a visual form  Transform text or numbers into a picture  Let the viewer ‘see’ the patterns, relationships and features of the data
  21. 21. Why visualization?  We are a visual species  one-third of the human brain is devoted to processing visual information  We are very good at recognizing visual cues, discerning visual patterns and identifying spatially encoded relationships
  22. 22. Why visualization?  Ability to comprehend vast amounts of data  Allows the perception of unanticipated emergent features  Problems with the data itself can often be quickly recognized  Helps to see both large-scale and small- scale features of the data  Facilitates hypothesis formation
  23. 23. Visualization as a tool in the research process Dr. John Snow (1813-1858) “Father of Epidemiology” Investigated the spread of disease during 1854 cholera epidemic in central London
  24. 24. How is visualization a research field?  Visualizations are around us all the time.  Are there really new and potentially better ways to represent data?
  25. 25. How is visualization a research field?  Some of the data sets we encounter are so complex that there aren’t presently good ways to visualize them  Example: Visualization zebra social behavior, Khairi Reda, EVL
  26. 26. Visualization of zebra social behavior
  27. 27. Visualization of senator voting patterns
  28. 28. How is visualization a research field?  Research developments that have changed how we understand human perception and cognition  Psychology  Learning Sciences  Cognitive Sciences  Changes in the visualization medium  Computer Science  New Media / Art
  29. 29. What is ‘visual analytics’?  Visual Analytics  Discipline that examines approaches to enabling analytical reasoning through interactive visual interfaces.  Allows a researcher to explore, search, filter, transform the data and ‘see’ the result
  30. 30. Interactive visualizations  Researcher input determines what is displayed  Lets user explore data
  31. 31. Goal of visual analytics  Understand the analytical research process in a discipline  Examine the cognitive demands of this research  Develop interactive visual paradigms that integrate naturally into the workflow of researchers in a scientific discipline.  Put information into a context that permits understanding
  32. 32. EVL and visual analytics
  33. 33. Research with Monsanto  Thesis:The medium for which most visualization tools are designed, the small screen of a single personal computer, serve as a limitation for exploration, information juxtaposition and collaboration.  How can we better facilitate the transformation of data into knowledge using large, high-resolution displays as a ‘lens’ to focus and illuminate biological datasets
  34. 34. Review  A large, high-resolution collaborative platforms is coming to Chesterfield  As a graduate student, I hope to leverage this technology to enable data visualization and advance the research pipeline at Monsanto.
  35. 35. JillianAurisano Electronic Visualization Lab Department of Computer Science, University of Illinois, Chicago Credits: Andrew Johnson’s visualization course websites EVL webpages Reda, Khairi et al. Visualizing the Evolution of Community Structures in Dynamic Social Networks. IEEE Symposium on Visualization, 2011
  36. 36. View data with full resolution  Scenario: Maize genome with 30k genes  Tiled display wall with 8106x2304 pixels