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Michael StonebrakerSpeaker:
Michael Stonebraker, Adjunct Professor of Computer Science at M.I.T, Co-Director of the new Intel Science and Technology Center, CTO of VoltDB and Paradigm4, Inc.

Presentation Title:
Scalable Complex Analytics and DBMSs

Abstract:
We first discuss the DBMS requirements for applications requiring complex analytics, such as predictive modeling, data clustering and machine learning. We also discuss several markets with these kinds of needs. We further assert that such requirements will become more prevalent off into the future. Then, we turn to the various approaches to meeting these needs, and present the design of SciDB, an array-oriented DBMS with support for complex analytics. We finish by describing the mechanism used to connect R and SciDB in a seamless fashion.

Bio:
Dr. Stonebraker has been a pioneer of data base research and technology for more than a quarter of a century. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at M.I.T. he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, and the H-Store transaction processing engine. Currently, he is working on science-oriented DBMSs, OLTP DBMSs, and scalable data curation. He is the founder of five venture-capital backed startups, which commercialized his prototypes. Presently he serves as Chief Technology Officer of VoltDB and Paradigm4, Inc.

Professor Stonebraker is the author of scores of research papers on data base technology, operating systems and the architecture of system software services. He was awarded the ACM System Software Award in 1992, for his work on INGRES. Additionally, he was awarded the first annual Innovation award by the ACM SIGMOD special interest group in 1994, and was elected to the National Academy of Engineering in 1997. He was awarded the IEEE John Von Neumann award in 2005, and is presently an Adjunct Professor of Computer Science at M.I.T, where he is co-director of the new Intel Science and Technology Center focused on big data.

Subutai AhmadSpeaker:
Subutai Ahmad, VP of Engineering at Numenta

Presentation Title:
Automated Streaming Data Analysis: It’s Not The Same Game

Abstract:
Data is moving from the human scale to the machine scale. Every server, every building, every store, every device generates a continuous stream of information that is ever changing and potentially valuable. Historically, big data analytic systems require storing data for later batch analysis and manual modeling by a human expert. This is inefficient and cannot scale. Instead there is a growing need to rapidly create adaptive models that accept streaming data sources and can take instant action. In order to really scale, such systems must be highly automated and automatically adapt to changing conditions.

In this talk I will go over these issues and their impact on big data. I will describe a new product, called Grok, a cloud based system designed for streaming analytics. Using Grok, you can deploy learning models on the fly, 100-1000X faster than legacy systems. The models require no human parameter tweaking and adapt continuously. I will describe example applications such as anomaly detection, energy operations, cloud server capacity planning and online advertising. As the number of data sources grow, and the velocity of data increases, automated learning systems such as Grok will play an increasingly important role in the future of machine learning and big data analytics.

Bio:
Subutai Ahmad brings experience in real time systems, computer vision and machine learning. At Numenta Subutai oversees technology and product development. Prior to Numenta, Subutai served as VP Engineering at YesVideo, Inc. He helped grow YesVideo from a three-person start-up to a leader in automated digital media authoring. YesVideo’s real time video analysis systems have been deployed internationally on a variety of platforms: large scale distributed clusters, retail minilabs, and set-top boxes. Subutai holds a Bachelor’s degree in Computer Science from Cornell University, and a PhD in Computer Science from the University of Illinois at Urbana-Champaign.

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