Learning Efficiently Over Heterogeneous Databases: Sampling and Constraints to the Rescue

Abstract

Given a relational database and training examples for a target relation, relational learning algorithms learn a definition for the target relation in terms of the existing relations in the database. We propose a relational learning system called CastorX, which learns efficiently across multiple heterogeneous databases. The user specifies connections and relationships between different databases using a set of declarative constraints called matching dependencies (MDs). Each MD connects tuples across multiple databases that are related and can meaningfully join but the values of their join attributes may not be equal due to the different representations of these values in different databases. CastorX leverages these constraints during learning to find the information relevant to the training data and target definition across multiple databases. Since each tuple in a database may be connected to too many tuples in other databases according to an MD, the learning process will become very slow. Hence, CastorX uses sampling techniques to learn efficiently and output accurate definitions.