Abstract. The heterogeneity nature of XML data creates the need for approximate query answering. In this paper, we present an XML system that cooperates with users to provide user-...
We study complexity and approximation of queries in an expressive query language for probabilistic databases. The language studied supports the compositional use of confidence com...
One challenge faced by many Inductive Logic Programming (ILP) systems is poor scalability to problems with large search spaces and many examples. Randomized search methods such as ...
Abstract. We provide improved approximation algorithms for the minmax generalization problems considered by Du, Eppstein, Goodrich, and Lueker [1]. In min-max generalization proble...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...