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.

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. This work is supported by the National Natural Science, Foundation of China (Grant No.69973050) and the National NKBRSF ‘973’ Project of China (Grant No.2001CCA03000). FENG Yu received her M.S. degree and joined the faculty of school of information, Renmin Univesity of China, in 1996. She is currently an Ph.D. candidate in the Institute of Computing Technology, the Chinese Academy of Sciences. Her research interests are data warehousing, OLAP and data mining. For the biography ofWANG Shan please refer to P.396, No.4, Vol.17 of this journal.