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The Big Data Revolution: Transforming Science and Society Through eScience

colman avatar
colman
32 diapositives · 487 Views · Aug 04, 2014

In July 2013, Ed Lazowska discussed the exponential growth of technology and algorithms fueling the "big data" revolution at the University of Washington. With advancements in sensors, storage costs, network bandwidth, and computational scalability, we can process vast amounts of data seamlessly. eScience utilizes machine learning, data mining, and innovative techniques for real-world applications like fraud detection, predictive analytics, and scientific discovery. As data continues to drive decision-making across various sectors, institutions must embrace eScience to remain competitive.

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  1. “Big Data” andData-Intensive Science (eScience) Ed Lazowska Bill & Melinda Gates Chair in Computer Science & Engineering University of Washington July 2013

  2. Exponential improvements in technology and algorithms are enabling the “big data” revolution • A proliferation of sensors • Think about the sensors on your phone • More generally, the creation of almost all information in digital form • It doesn’t need to be transcribed in order to be processed • Dramatic cost reductions in storage • You can afford to keep all the data • Dramatic increases in network bandwidth • You can move the data to where it’s needed

  3. Dramatic cost reductions and scalability improvements in computation • With Amazon Web Services, or Google App Engine, or Microsoft Azure, 1000 computers for 1 day cost the same as 1 computer for 1000 days! • Dramatic algorithmic breakthroughs • Machine learning, data mining – fundamental advances in computer science and statistics

  4. Some examples of “big data” in action • Collaborative filtering

  5. Fraud detection

  6. Price prediction

  7. Hospital re-admission prediction

  8. Travel time prediction under specific circumstances

  9. Sports

  10. Home energy monitoring

  11. Larry Smarr, UCSD John Guttag & Collin Stultz, MIT Gordon Bell, Microsoft Research Google self-driving car

  12. Speech recognition

  13. Machine translation • Speech -> text • Text -> text translation • Text -> speech in speaker’s voice http://www.youtube.com/watch?v=Nu-nlQqFCKg&t=7m30s 7:30 – 8:40

  14. Scientific discovery Gene Sequencing Ocean Observatories Initiative Large Hadron Collider Large Synoptic Survey Telescope

  15. Presidential campaigning

  16. Electoral forecasting

  17. Real data-driven decision-making (vs. MBA baloney) for every sector!

  18. eScience: Sensor-driven (data-driven) science and engineering Transforming science (again!) Jim Gray

  19. TheoryExperimentObservation

  20. TheoryExperimentObservation

  21. TheoryExperimentObservation [John Delaney, University of Washington]

  22. TheoryExperimentObservation ComputationalScience

  23. TheoryExperimentObservation ComputationalScience eScience

  24. eScience is driven by data more than by cycles • Massive volumes of data from sensors and networks of sensors • Apache Point telescope, SDSS • 80TB of raw image data (80,000,000,000,000 bytes) • over a 7 year period

  25. Large Synoptic Survey Telescope (LSST) • 40TB/day • (an SDSS every two days), • 100+PB in its 10-year lifetime • 400mbps sustained data rate between • Chile and NCSA

  26. Large Hadron Collider • 700MB of data • per second, • 60TB/day, 20PB/year

  27. Illumina • HiSeq 2000 Sequencer • ~1TB/day • Major labs have 25-100 of these machines

  28. Regional Scale Nodes of the NSF Ocean Observatories Initiative • 1000 km of fiber optic cable on the seafloor, connecting thousands of chemical, physical, and biological sensors

  29. The Web • 20+ billion web pages x 20KB = 400+TB • One computer can read 30-35 MB/sec from disk => 4 months just to read the web

  30. eScience is about the analysis of data • The automated or semi-automated extraction of knowledge from massive volumes of data • There’s simply too much of it to look at • It’s not just a matter of volume • Volume • Rate • Complexity / dimensionality

  31. eScience utilizes a spectrum of computer science techniques and technologies • Sensors and sensor networks • Backbone networks • Databases • Data mining • Machine learning • Data visualization • Cluster computing at enormous scale

  32. eScience will be pervasive • Simulation-oriented computational science has been transformational, but it has been a niche • As an institution (e.g., a university), you didn’t need to excel in order to be competitive • eScience capabilities must be broadly available in any institution • If not, the institution will simply cease to be competitive