About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
Proceedings - International Conference on Data Engineering
Paper
fAST refresh using Mass Query Optimization
Abstract
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.