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The concepts of 'data warehousing' and 'on-line analytical processing' have seen a growing interest in the research and commercial product community. Today, the trend moves away from complex centralized data warehouses to distributed data marts integrated in a common conceptual schema. However, as the first part of this paper demonstrates, there are many problems and little solutions for large distributed decision support systems in worldwide operating corporations. After showing the benefits and problems of the distributed approach, this paper outlines possibilities for achieving performance in distributed online analytical processing. Finally, the architectural framework of the prototypical distributed OLAP system CUBESTAR is outlined.

Automatic summary tables (ASTs), more commonly known as materialized views, are widely used to enhance query performance, particularly for aggregate queries. Such queries access a huge number of rows to retrieve aggregated summary data while performing multiple joins in the context of a typical data warehouse star schema. To keep ASTs consistent with their underlying base data, the ASTs are either immediately synchronized or fully recomputed. This paper proposes an optimization strategy for simultaneously refreshing multiple ASTs, thus avoiding multiple scans of a large fact table (one pass for AST computation). A query stacking strategy detects common sub-expressions using the available query matching technology of DB2. Since exact common sub-expressions are rare, the novel query sharing approach systematically generates common subexpressions for a given set of 'related' queries, considering different predicates, grouping expressions, and sets of base tables. The theoretical framework, a prototype implementation of both strategies in the IBM DB2 UDB/UWO database system, and performance evaluations based on the TPC/R data schema are presented in this paper.