Abstract
Multidimensional clustering (MDC) has emerged as a powerful new physical database design technique. It has emerged onto the industrial scene with a full implementation in DB2. It offers potentially huge performance improvement for query processing and roll-out by dramatically reducing the input/output (I/O) processing performed. MDC has been motivated to a large extent by the spectacular growth of relational datathat has spurred the continual research and development of improved techniques for handling large data sets and complex queries. In particular, online analytical processing (OLAP) and decision support systems (DSS) have become popular for data mining and business analysis. OLAP and DSS systems are characterized by multidimensional analysis of compiled enterprise data, and typically include transactional queries including group-by, aggregation, range queries, cube, roll-up, and drill-down. MDC techniques have been shown to have very significant performance benefits for complex workloads. The present industrial implementation of MDC is in IBM's DB2 UDB for Linux, UNIX, and Windows. Prior to the IBM implementation most of the research literature on MDC had focused on how to better design database storage structures, rather than on how to select the clustering dimensions.
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