Parallel knowledge discovery using domain generalization graphs

Abstract

Multi-Attribute Generalization is an algorithm for attribute-oriented induction in relational databases using domain generalization graphs. Each node in a domain generalization graph represents a different way of summarizing the domain values associated with an attribute. When generalizing a set of attributes, we show how a serial implementation of the algorithm generates all possible combinations of nodes from the domain generalization graphs associated with the attributes, resulting in the presentation of all possible generalized relations for the set. We then show how the inherent parallelism in domain generalization graphs is exploited by a parallel implementation of the algorithm. Significant speedups were obtained using our approach when large discovery tasks were partitioned across multiple processors. The results of our work enable a database analyst to quickly and efficiently analyze the contents of a relational database from many different perspectives.