ACM Transactions on Database Systems · 2001 · 82 citations · 49 references
ComputingComputer ScienceInformation TechnologyData ManagementDatabasesStorage SystemsData WarehousingDatabase ManagementQuery OptimizationQuery ProcessingMaintenance Window OptimizationMaintenance Window PlanningMaterialized View MaintenanceMaterialized View SelectionMaterialized ViewsOlap Query OptimizationOlap Query ProcessingView Updating
Materialized aggregate views are redundant entities used to accelerate OLAP, and because of complex warehouse structures and diverse user query profiles, automated tools are needed to simplify view selection and management. The article introduces DynaMat, a system that manages dynamic collections of materialized aggregate views in a data warehouse. DynaMat stores computed aggregates on dedicated disk space, executes queries independently or in bundled multi‑query expressions by exploiting dependencies for optimization, and during updates reconciles the current view set and refreshes the most beneficial subset within a maintenance window, deriving efficient update plans based on policies and dependencies.
Materialized aggregate views represent a set of redundant entities in a data warehouse that are frequently used to accelerate On-Line Analytical Processing (OLAP). Due to the complex structure of the data warehouse and the different profiles of the users who submit queries, there is need for tools that will automate and ease the view selection and management processes. In this article we present DynaMat, a system that manages dynamic collections of materialized aggregate views in a data warehouse. At query time, DynaMat utilizes a dedicated disk space for storing computed aggregates that are further engaged for answering new queries. Queries are executed independently or can be bundled within a multiquery expression. In the latter case, we present an execution mechanism that exploits dependencies among the queries and the materialized set to further optimize their execution. During updates, DynaMat reconciles the current materialized view selection and refreshes the most beneficial subset of it within a given maintenance window. We show how to derive an efficient update plan with respect to the available maintenance window, the different update policies for the views and the dependencies that exist among them.
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