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New England Database Society

Friday, April 08, 2011

sponsored by Netezza Corporation

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   ASTERIX: Towards a Scalable, Semistructured Data Platform for Evolving World Models

 Michael Carey 
University of California at Irvine

Friday, April 08, 2011, 4PM
Volen 101, Brandeis University

(preceded by a wine and cheese reception at 3:00 pm, and followed by dinner at 6:00 pm)


ASTERIX is a new data-intensive storage and computing platform project spanning UC Irvine, UC Riverside, and UC San Diego. This talk will provide an overview of the effort, starting with its main objective - namely, the storage and analysis of data pertaining to "evolving world models". We will describe the requirements and associated challenges and explain how the project is addressing them. We will provide a technical overview of ASTERIX, covering its architecture, its user model for data and queries, and its approach to scalable query processing and data management. ASTERIX utilizes a new scalable runtime computational platform called Hyracks, which will also be described. We will relate this work to the current state of the art and describe the research challenges that we are currently tackling as well as those that lie further down the road. Finally, we will describe our plans for measuring the success of the project and of the open source software components that are its intended deliverables.

Speaker's Bio:

Mike Carey is currently a Bren Professor of Information and Computer Sciences at UC Irvine. Prior to rejoining academia in 2008, he worked at BEA Systems as the chief architect and an engineering director for BEA's AquaLogic Data Services Platform team. Carey also spent a number of years as a Professor at the University of Wisconsin-Madison, at IBM Almaden as a database researcher/manager, and as a Fellow at e-commerce software startup Propel Software. Carey is an ACM Fellow, a member of the National Academy of Engineering, and a recipient of the ACM SIGMOD Edgar F. Codd Innovations Award. His current research interests are centered around data-intensive computing and scalable data management.

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