@techreport{TD:100535,
	att_abstract={{With the availability of very large databases, an exploratory
query can easily lead to a vast answer set, typically based on
an answer�s relevance (i.e., top-k, tf-idf ) to the user query.
Navigating through such an answer set requires huge effort
and users give up after perusing through the first few answers, 
thus some interesting answers hidden further down the 
answer set can easily be missed. An approach to address 
this problem is to present the user with the most diverse 
among the answers based on some diversity criterion.
In this demonstration we present DivDB, a system we built
to provide query result diversification both for advanced and
novice users. For the experienced users, who may want to
test the performance of existing and new algorithms, we
provide an SQL-based extension to formulate queries with
diversification. As for the novice users, who may be more
interested in the result rather than how to tune the various
algorithms� parameters, the DivDB system allows the user
to provide a �hint� to the optimizer on speed vs. quality of
result. Moreover, novice users can use an interface to dy-
namically change the tradeoff value between relevance and
diversity in the result, and thus visually inspect the result as
they interact with this parameter. This is a great feature to
the end user because finding a good tradeoff value is a very
hard task and it depends on several variables (i.e., query
parameters, evaluation algorithms, and dataset properties).
In this demonstration we show a study of the DivDB system
with two image databases that contain many images of the
same object under different settings (e.g., different camera
angle). We show how the DivDB helps users to iteratively
inspect diversification in the query result, without the need
to know how to tune the many different parameters of the
several existing algorithms in the DivDB system.}},
	att_authors={mh6516, ds8961},
	att_categories={},
	att_copyright={{VLDB Foundation}},
	att_copyright_notice={{The definitive version was published in Very Large Databases, 2011. {{, 2011-08-29}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:100535},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:100535_DS1_2011-09-04T19:19:12.214Z.pdf},
	author={Marcos R. Vieira and Humberto L. Razente and Maria C. N. Barioni and Marios Hadjieleftheriou and Divesh Srivastava and Caetano Traina Jr. and Vassilis J. Tsotras},
	institution={{VLDB Conference}},
	month={August},
	title={{DivDB: A System for Diversifying Query Results}},
	year=2011,
}