  Most enterprise data is distributed in multiple relational databases with
expert-designed schema. Using traditional single-table machine learning
techniques over such data not only incur a computational penalty for converting
to a 'flat' form (mega-join), even the human-specified semantic information
present in the relations is lost. In this paper, we present a practical,
two-phase hierarchical meta-classification algorithm for relational databases
with a semantic divide and conquer approach. We propose a recursive, prediction
aggregation technique over heterogeneous classifiers applied on individual
database tables. The proposed algorithm was evaluated on three diverse
datasets, namely TPCH, PKDD and UCI benchmarks and showed considerable
reduction in classification time without any loss of prediction accuracy.
