How Visualization Matches the Vision of a World Class Computer Science Department Robert S Laramee

Transcription

1 How Visualization Matches the Vision of a World Class Computer Science Department Robert S Laramee

2 The Age of Digital Enlightenment Real World Challenge Physical (Analogue) Approach Not feasible Digital Solution Data Chaos Visualization Cosmos Start: Problem, Goal, Challenge Propose physical or analogue solution Physical solution is too expensive or impossible Digital hardware/ software solution is proposed Digital solution leads to data chaos 1

3 The Universal Big Data Story (and Quandary) Computational Fluid Dynamics (CFD) Engineers: simulation data Physicists and Astronomers: experiments & images Marine Biologists and Bio-Chemists: accelerometers, simulations Psychologists, Sociologists, Criminologists: EEG, Surveys Sports Scientists: video & events Journalists, Humanities: writing & text data Banks etc: customer and finance data Call Centers: customer and employee data Retail: customers, sales, financial, stock data Web sites: Web traffic data, internet data, cybersecurity Transportation: air traffic, trains, automobile, cycling, walking data, smart city data Health Care: NHS, Public Health England, electronic health records You: photos, videos, and files Governments and Councils: public sector data 4 Everyone and Everywhere People like to collect

4 Visualization is Good For Visualization, in order to: Explore no a-priori knowledge about the data, Visualization serves to explore the data Analyze There is a hypothesis, Visualization Serves to Confirm/Refute (Disprove) Present The data characteristics are well known, Visualization. Serves to communicate the results Accelerate Gain a quick overview, fast analysis 3

5 Examples of Digital Enlightenment Physics and Foam Visualization Understanding Call Centers and Their Customers Geography and Health Science Engineering and Automotive Engine Design Computational Biology and Molecular Dynamics Sports Scientists: video & events Marine Biology and Animal Tracking The Search For Wet, Green Energy 4

6 Swirl Example and from Tumble Physics: Motion FoamVis Goal: Understanding foam dynamics Analogue Solution: experimental methods Digital (Software) Solution: foam simulation à millions of events Constriction simulation: average velocity over all time steps 5

7 Example: Geography and Health Science Goal: Understanding Public Health Analogue Solution: Hire More NHS Staff Digital (Software) Solution: collect diagnoses à millions of records, multivariate, couple with geography #

8 Example: Geography and Health Science 7

9 Example: Computational Biology Goal: understanding biology at molecular level Analogue Solu-on: experiments, Nuclear Magne8c Resonance (NRM) spectroscopy Digital Solu-on: molecular dynamics simula8on (MDS) 8 9

10 Example: BioScience Challenge: understanding marine wildlife behavior Analogue Solu-on: follow the animals Digital Solu-on: Biologists at Swansea adach sensors To gather data on: accelera8on, temperature, pressure, etc 9 10

11 Swirl Example: and Visualization Tumble of Call Center Motion Data Goal: Understanding call center behavior Analogue Solution: hire more people à $$$ Digital (Software) Solution: event database à millions of events 11 The Search for Swirl and Tumble Motion

12 Example: Investigating Swirl and Tumble Flow with a Swirl and Tumble Motion Comparison of Techniques Goal: swirl motion characterized by motion about cylinder-aligned axis more stable (easier) Analogue Solution: build engine Goal: tumble motion characterized by motion about axis orthogonal to cylinder unstable, more difficult Digital Solution: CFD 11

13 Example: Glyph-Sorting for Interactive Visualization of Sports Video Goal: win more games Analogue Solu-on: hire more people, study videos Digital Solu-on: record event data à thousands of events 12 13

14 Example: The Search for Wet, Green Energy Goal: Understanding Flow Around Turbines for optimal placement Analogue Solution: Install Real Turbines Digital (Software) Solution: Computational Fluid Dynamics Data 5

15 Acknowledgements Thank you for your attention. Any Questions? Thanks to the following people: Naif Alharbi, Paul Brookes, Matthieu Chavant, Tom Cheesman, Min Chen, David Chung, Nick Croft, Tony D Cruz, Iwan Griffiths, Christoph Garth, Edward Grundy, Hans Hagen, Helwig Hauser, Mark W Jones, Robert Kosara, Phillip Legg, Ian Masters, Matthew Parry, Zhenmin Peng, Richard Roberts, Juergen Schneider, Gary Smith, Chao Tong, Daniel Weiskopf, Rory P Wilson, 13