Deriving Probabilistic Databases With Inference Ensembles

Many real-world applications deal with uncertain or missing data, prompting a surge of activity in the area of probabilistic databases. A shortcoming of prior work is the assumption that an appropriate probabilistic model, along with the necessary probability distributions, is given. This paper addresses this shortcoming by presenting a framework for learning a set of inference ensembles, termed meta-rule semi-lattices, or MRSL, from the complete portion of the data.

Provided by: University of Pennsylvania Topic: Data Management Date Added: Nov 2010 Format: PDF

Find By Topic