Indexing and selecting hierarchical business logic
Alessandra Loro, Anja Gruenheid, Donald Kossmann, Damien Profeta, Philippe Beaudequin · Proceedings of the VLDB Endowment · 2015
Business rule management is the task of storing and maintaining company-specific decision rules and business logic that is queried frequently by application users. These rules can impede efficient query processing when they require the business rule engine to resolve semantic hierarchies. To address this problem, this work discusses hierarchical indexes that are performance and storage-conscious. In the first part of this work, we develop a tree-based hierarchical structure that represents client-defined semantic hierarchies as well as two variants of this structure that improve performance and main memory allocation. The second part of our work focuses on selecting the top rules out of those retrieved from the index. We formally define a priority score-based decision scheme that allows for a conflict-free rule system and efficient rule ranking. Additionally, we introduce a weight-based lazy merging technique for rule selection. All of these techniques are evaluated with real world and synthetic data sets.