att_abstract={{The amount of useful information available on the Web has been 
growing at a dramatic pace in recent years and people rely more 
and more on the Web to fulfill their information needs. In this 
paper, we study truthfulness of Deep Web data in two domains 
where we believed data are fairly clean and data quality is 
important to people's lives: Stock and Flight. To our surprise, 
we observed a large amount of inconsistency on data from 
different sources and also some sources with quite low accuracy. 
We further applied on these two data sets state-of-the-art data 
fusion methods that aim at resolving conflicts and finding the 
truth, analyzed their strengths and limitations, and suggested
promising research directions. We wish our study can increase 
awareness of the seriousness of conflicting data on the Web
and in turn inspire more research in our community to tackle
this problem.}},
	att_authors={ds8961, kl5165, xd0649},
	att_categories={C_NSS.2, C_IIS.5, C_IIS.6},
	att_copyright={{VLDB Foundation}},
	att_copyright_notice={{The definitive version was published in Very Large Databases, 2012. {{, 2013-08-26}}
	author={Divesh Srivastava and Kenneth Lyons and Xin Dong and Xian Li and Weiyi Meng},
	title={{Truth Finding on the Deep Web: Is the Problem Solved?}},