Answering approximate range aggregate queries on OLAP data cubes with probabilistic guarantees

Approximate range aggregate queries are one of the most frequent and useful kinds of queries for Decision Support Systems (DSS). Traditionally, sampling-based techniques have been proposed to tackle this problem. However, its effectiveness will degrade when the underlying data distribution is skewed. Another approach based on the outlier management can limit the effect of data skew but fails to address other requirements of approximate range aggregate queries, such as error guarantees and query processing efficiency. In this paper, we present a technique that provide approximate answers to range aggregate queries on OLAP data cubes efficiently with theoretical error guarantees, Our basic idea is to build different data structures for outliers and the rest of the data. Experimental results verified the effectiveness of our proposed methods.