Compressed data cube for approximate OLAP query processing

Approximate query processing has emerged as an approach to dealing with the huge data volume and complex queries in the environment of data warehouse. In this paper, we present a novel method that provides approximate answers to OLAP queries. Our method is based on building a compressed (approximate) data cube by a clustering technique and using this compressed data cube to provide answers to queries directly, so it improves the performance of the queries. We also provide the algorithm of the OLAP queries and the confidence intervals of query results. An extensive experimental study with the OLAP council benchmark shows the effectiveness and scalability of our cluster-based approach compared to sampling.