Dealing with MASSIVE Data

Speaker:	Dr. Ke YI
		Assistant Professor
		Department of Computer Science & Engineering
		Hong Kong University of Science & Technology

Title:		"Dealing with MASSIVE Data"

Date:		Monday, 8 October 2007

Time:		4:00pm - 5:00pm

Venue:		Lecture Theatre F
		(Leung Yat Sing Lecture Theatre, near lift nos. 25/26)
		HKUST
Abstract:

We live in an age of exploding information.  Massive data is being
generated on a daily basis from business, technology, to scientific
research: network traffic, transaction records, web logs, terrain data,
genomic data, etc.  These data sets now easily reach the order of
gigabytes or terabytes, and will continue to rise at ever-increasing
rates. Scalability is essential for the algorithms that handle these
massive data sets.  The traditional RAM model does not capture well the
behavior of these algorithms anymore.

This talk will give an introductory overview of three computation models
developed in the last two decades: the external memory model, the
cache-oblivious model, and the streaming model.  All of them aim at
addressing the discrepancy between the scale of the data and the limited
computing resources.  I will review both the state-of-art theoretical
development under these models, as well as their practical applications in
areas like databases and networking.


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Biography:

Ke YI is an Assistant Professor at the Department of Computer Science and
Engineering, HKUST.  He received his BS from Tsinghua University in 2001,
and PhD from Duke University in 2006, both in Computer Science.  Prior to
joining HKUST this August, he spent a year at AT&T Labs as a Research
Specialist.  Ke's research mainly focuses on algorithms and data
structures for massive data sets, but he is also broadly interested in
many algorithmic problems arising from databases.