Conceptual-relational mappings between conceptual models and relational schemas have been used increasingly to achieve interoperability or overcome impedance mismatch in modern dat...
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...
The aim of this short paper is to present a general method of using background knowledge to impose constraints in conceptual clustering of object-attribute relational data. The pr...
Ranking queries are essential tools to process large amounts of probabilistic data that encode exponentially many possible deterministic instances. In many applications where unce...