GPIVOT: efficient incremental maintenance of complex ROLAP views

Data warehousing and on-line analytical processing (OLAP) are essential for decision support applications. Common OLAP operations include for example drill down, roll up, pivot and unpivot. Typically, such queries are fairly complex and are often executed over huge volumes of data. The solution in practice is to use materialized views to reduce the query cost. Utilizing materialized views that incorporate not just traditional simple SELECT-PROJECT-JOIN operators but also complex OLAP operators such as pivot and unpivot is crucial to improve the OLAP query performance but as of now unexplored topic. In this work, we demonstrate that the efficient maintenance of views with pivot and unpivot operators requires the definition of more generalized operators, which we call GPIVOT and GUNPIVOT. We propose rewriting rules, combination rules and propagation rules for such operators. We also design a novel view maintenance framework for applying these rules to obtain an efficient maintenance plan. Our query transformation rules are thus dual purpose serving both view maintenance and query optimization. This paves the way for the inclusion of the GPIVOT and GUNPIVOT into any DBMS engine.