On-Line Analytical Processing (OLAP) based on a dimensional view of data is being used increasingly for the purpose of analyzing very large amounts of data. To improve query performance, modern OLAP systems use a technique known as practical pre-aggregation, where select combinations of aggregate queries are materialized and re-used to compute other aggregates; full preaggregation, where all combinations of aggregates are materialized, is infeasible. However, this reuse of aggregates is contingent on the dimension hierarchies and the relationships between facts and dimensions satisfying stringent constraints, which severely limits the scope of practical preaggregation. This paper significantly extends the scope of practical pre-aggregation to cover a much wider range of realistic situations. Specifically, algorithms are given that transform “irregular” dimension hierarchies and fact-dimension relationships, which often occur in real-world OLAP applications, into well-behaved str...
Torben Bach Pedersen, Christian S. Jensen, Curtis