ive. This characterization leads to model-based abstractions and representation design techniques as potential solutions. Many of the existing approaches to coping with data overlo...
David D. Woods, Emily S. Patterson, Emilie M. Roth
With the enormous and still growing amount of data and user interaction on the Web, it becomes more and more necessary for data consumers to be able to assess the trustworthiness ...
This paper explores an inherent tension in modeling and querying uncertain data: simple, intuitive representations of uncertain data capture many application requirements, but the...
Anish Das Sarma, Omar Benjelloun, Alon Y. Halevy, ...
We present a new machine learning framework called "self-taught learning" for using unlabeled data in supervised classification tasks. We do not assume that the unlabele...
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin ...
Duplicate detection determines different representations of realworld objects in a database. Recent research has considered the use of relationships among object representations t...