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[jeffery@CS.Berkeley.EDU: DB Seminar, October 27th: Amol Deshpande]

From: elein <elein(at)varlena(dot)com>
To: sfpug(at)postgresql(dot)org
Subject: [jeffery@CS.Berkeley.EDU: DB Seminar, October 27th: Amol Deshpande]
Date: 2006-10-25 18:23:01
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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, October 27th: Amol Deshpande

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

Friday, October 27th, 2006
380 Soda Hall
1-2:30pm

Speaker:
Amol Deshpande, University of Maryland 	

Title:
MauveDB: Managing Uncertain Data using Statistical Models  	

Abstract:
Real-world data, especially that generated by distributed measurement
infrastructures such as wireless sensor networks, tends to be
incomplete, imprecise, and erroneous, and hence rarely usable in its
raw form. The traditional approach to dealing with this problem is to
first synthesize (filter) such data using a statistical or a
probabilistic model, thus resulting in a more robust interpretation of
the data. However current database systems do not provide adequate
support for statistical modeling of data, especially when those models
need to be frequently updated as new data arrives in the system. Hence
most scientists and engineers, who depend on models for managing their
data, do not use database systems for archival or querying at all; at
best, databases serve as a persistent raw data store. In this talk, I
will present our approach to integrating statistical and probabilistic
models into database systems, in the context of data management in
wireless sensor networks. I will first present a data acquisition
approach for wireless sensor networks that demonstrates how models can
be used both to provide more meaningful answers to user queries, and
to significantly reduce the energy cost of acquiring data from the
underlying sensing devices. I will then present our recent work on the
"MauveDB" system, which uses an abstraction called "model-based views"
to seamlessly integrate models into traditional relational database
systems.

Bio:
Amol Deshpande is an Assistant Professor at the University of Maryland
at College Park. He received his PhD from UC Berkeley in 2004. His
research interests are adaptive query processing, sensor network data
management, and statistical modeling of data.


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