An Improved OLAP Join and Aggregate Algorithm Based on Dimension Hierarchy

The OLAP (online analytical processing) queries are always involved with queries on the massive dataset. As a result, how to perform multi-table join and aggregate operations becomes the key issue. A join and aggregate algorithm based on dimension hierarchy (JABDH) is proposed in this paper. Considering the semantic characteristic which is not in all the dimension hierarchies, dimension hierarchical encoding is used to retrieve the matching dimension hierarchies and evaluate the set of query ranges for semantic dimension hierarchies. To improve the efficiency of multi-table join and aggregate operations for non-semantic dimensional hierarchies, join and aggregate operations are translated into bitmapped join index of fact table. The performance analysis and experimental results show that JABDH has improved the speed of queries and the efficiency of the OLAP queries.