Evaluation of top-k OLAP queries using aggregate R-trees

A top-κ OLAP query groups measures with respect to some abstraction level of interesting dimensions and selects the κ groups with the highest aggregate value. An example of such a query is "find the 10 combinations of product-type and month with the largest sum of sales". Such queries may also be applied in a spatial database context, where objects are augmented with some measures that must be aggregated according to a spatial division. For instance, consider a map of objects (e.g., restaurants), where each object carries some non-spatial measure (e.g., the number of customers served during the last month). Given a partitioning of the space into regions (e.g., by a regular grid), the goal is to find the regions with the highest number of served customers. A straightforward method to evaluate a top-κ OLAP query is to compute the aggregate value for each group and then select the groups with the highest aggregates. In this paper, we study the integration of the top-κ operator with the aggregate query processing module. For this, we make use of spatial indexes, augmented with aggregate information, like the aggregate R-tree. We device a branch-and-bound algorithm that accesses a minimal number of tree nodes in order to compute the top-κ groups. The efficiency of our approach is demonstrated by experimentation.