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[jeffery@CS.Berkeley.EDU: DB Seminar, December 8th, Jun Yang]

From: elein <elein(at)varlena(dot)com>
To: sfpug(at)postgresql(dot)org
Subject: [jeffery@CS.Berkeley.EDU: DB Seminar, December 8th, Jun Yang]
Date: 2006-12-07 01:38:32
Message-ID: 20061207013832.GQ24367@varlena.com (view raw or flat)
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----- Forwarded message from Shawn Jeffery <jeffery(at)CS(dot)Berkeley(dot)EDU> -----

From: Shawn Jeffery <jeffery(at)CS(dot)Berkeley(dot)EDU>
To: dblunch(at)triplerock(dot)CS(dot)Berkeley(dot)EDU
Subject: DB Seminar, December 8th, Jun Yang

New Directions in Database Research Seminar Series
(http://db.cs.berkeley.edu/dbseminar.php)

Friday, December 8th, 2006
380 Soda Hall
1-2:30pm

Speaker:
Jun Yang, Duke University 	

Title:
Scalable Continuous Query Processing and Result Dissemination  	

Abstract:
In contrast to traditional database queries that run once against a
database snapshot, continuous queries continuously generate new
results (or changes to results) as new data and updates arrive in
streams. Many applications, e.g., publish/subscribe systems, need to
handle a large number of long-standing continuous queries whose
results are needed across a wide-area network. The naive approach,
which checks each incoming data item against every query and sends a
separate notification for each affected query, is not scalable. While
there has been a considerable amount of work on continuous filters,
more complex queries, such as joins and aggregates, are more
challenging.

In this talk, I will first describe our recent results on processing a
large number of continuous joins. Our techniques are
input-sensitive---they exploit patterns in queries and data for
efficient processing. We demonstrate the advantage of this approach
over previously known techniques both theoretically and
experimentally.

The second problem I will address is how to disseminate the results of
many continuous queries efficiently to users over a network.
Traditional solutions are either database- or network-centric, but we
argue that there is a previously unexplored design space between these
two extremes, and we show how to achieve better scalability by
incorporating both database- and network-side considerations.

Bio:
Jun Yang received his B.A. from University of California at Berkeley
in 1995, and his Ph.D. from Stanford University in 2001. He is
currently an Assistant Professor of Computer Science at Duke
University. He is broadly interested in research on data management,
and is currently focusing on derived data maintenance, continuous
query systems, and sensor data processing. He is a recipient of the
National Science Foundation CAREER Award and the IBM Faculty Award.


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