@techreport{TD:101628,
	att_abstract={{Recently there has been a rapid increase in the number of data
sources and publicly accessible data services, such as cloud-based
data markets and data portals, that facilitate the collection, publishing
and trading of data. Data sources typically exhibit large
heterogeneity in the type and quality of data they provide. Unfortunately,
when the number of data sources is large, it is for users
to reason about the actual usefulness of sources and the trade-offs
between the benefits and costs of acquiring and integrating sources.
In this demonstration we present SourceSight, a system that allows
users to explore a large number of heterogeneous data sources,
and discover valuable sets of sources for diverse integration tasks.
SourceSight uses a novel multi-level source quality index that
enables effective source selection at different granularity levels, and
introduces a collection of new techniques to discover and evaluate
relevant sources for integration.}},
	att_authors={ds8961},
	att_categories={C_NSS.2},
	att_copyright={{ACM}},
	att_copyright_notice={{(c) ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM SIGMOD International Conference on Management of Data{{, 2016-06-25}}.}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:101628},
	att_url={http://web1-clone.research.att.com:81/techdocs_downloads/TD:101628_DS1_2015-05-08T14:23:42.828Z.pdf},
	author={Divesh Srivastava and Theodoros Rekatsinas and Amol Deshpande and Xin Luna Dong and Lise Getoor},
	institution={{ACM SIGMOD International Conference on Management of Data}},
	month={June},
	title={{SourceSight: Enabling Effective Source Selection}},
	year=2016,
}