Adaptive method for range top-k queries in OLAP data cubes

Abstract

In decision-support systems, the top k values are more informative than the max/min value. Unfortunately, the existing methods for range-max queries could not answer range top-k queries efficiently if applied directly. In this paper, we propose an efficient approach for range top-k processing, termed the Adaptive Pre-computed Partition Top method (APPT). The APPT method pre-computes a set of maximum values for each partitioned sub-block. The number of stored maximum values can be adjusted dynamically during run-time to adopt to the distribution of the query and the data. We show with experiments that our dynamic adaptation method improves in query cost as compared to other alternative methods.