[Colloquium] Reminder: today's talk by Igor Tatarinov

[Margery Ishmael]

DEPARTMENT OF COMPUTER SCIENCE - TALK

Date: Monday, February 23, 2004
Time: 2:30 p.m.
Place: Ryerson 251

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Speaker: IGOR TATARINOV, University of Washington

Url: http://www.cs.washington.edu/homes/igor/myweb/

Title:  Semantic Data Sharing with a Peer Data Management System

Abstract:

Data sharing is a ubiquitous problem. Large enterprises, governments,
research communities, and people with common interests are interested
in making their data available as well as accessing others' data.  The
World Wide Web (WWW) and Peer-to-Peer (P2P) networks have been quite
successful in providing an easy way for users to share their data.  A
serious limitation of these approaches is that they support keyword
queries only, rather than much richer database-style queries that are
important for many kinds of data sharing applications. Database-style
queries require that data have structure (semantics) whereas Web and
P2P data is essentially text.

Data Integration and Data Warehousing are known database technologies
that enable semantic data sharing. These technologies are rather
heavyweight, however. They impose a centralized mediator schema, which
impedes schema evolution and complicates the data sharing process.

In this talk, I will desribe Peer Data Management, a novel approach to
semantic data sharing. In a Peer Data Management System (PDMS), a peer
is associated with a schema that represents the peer's "view of the
world". Every peer defines semantic mappings to a few other peers. The
resulting semantic network enables querying over the entire shared
data using the user's local schema. The PDMS takes care of
reformulating (translating) the user query over the query peer into
queries over other peers. I will describe an algorithm for query
reformulation in a PDMS and a number of optimization techniques that
significantly improve the scalability of the algorithm.

Host: ANNE ROGERS

*Refreshments will follow the talk in Ryerson 255*

People in need of assistance should call 773-834-8977 in advance.