Analyzing Massive Datasets

By Alexander Gray PhD, CTO at Skytree

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About the Whitepaper

As the interest in analytics on massive datasets grows across virtually all industries and fields, common questions arise, such as:

  • What is the difference between simple and advanced analytics?
  • How domain-specific is advanced analytics?
  • Can existing Commercial solutions really handle massive data?
  • Can state-of-the-art advanced analytics be done on massive data?

In this brief whitepaper, we’ll address the growing data science problem of doing fast, accurate, scalable analytics on big data in the commercial space. In particular, we’ll attempt to bridge the large gap between the world of academic research in statistical methods and algorithms and the world of commercial offerings in the analytics space.

About the Author

Alexander Gray PhD, CTO of Skytree, received a bachelor’s degrees in Applied Mathematics and Computer Science from the University of California, Berkeley and a PhD in Computer Science from Carnegie Mellon University. He began working with massive scientific datasets in 1993 (long before the current fashionable talk of “big data”) at NASA’s Jet Propulsion Laboratory in its Machine Learning Systems Group. High-profile applications of his large-scale ML algorithms have been described in staff-written articles in Science and Nature, including contributions to work selected by Science as the Top Scientific Breakthrough of 2003. He has won or been nominated for a number of best paper awards in statistics and data mining and is a recipient of the National Science Foundation CAREER Award in 2009. He gives invited tutorial lectures on massive-scale data analysis at the top data analysis research conferences, government agencies, and corporations, and is a member of the prestigious National Academy of Sciences Committee on the Analysis of Massive Data. He is currently a professor in the College of Computing at Georgia Tech.

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