TSGV: a table-like structure-based greedy method for materialized view selection in data warehouses


Abstract: Since a data warehouse deals with huge amounts of data and complex analytical queries, online processing and answering to users' queries in data warehouses can be a serious challenge. Materialized views are used to speed up query processing rather than direct access to the database in on-line analytical processing. Since the large number and high volume of views prevents all of the views from being stored, selection of a proper subset of views to materialization is inevitable. Proposing an appropriate method for selecting the optimal subset of views for materialization plays an essential role in increasing the efficiency of responding to data warehouse queries. In this paper, a greedy materialized view selection algorithm is represented, which selects a proper set of views for materialization from a novel table-like structure. The information in this table-like structure is extracted from a multivalue processing plan. This table-like structure-based greedy view selection (TSGV) method is evaluated using the queries of an analytical database, and the query-processing and view maintenance costs of the selected subset are both considered in this evaluation. The experimental results show that TSGV operates better than previously represented methods in terms of time.

Keywords: Materialized view selection, multivalue processing plan, data warehouse, on-line analytical processing, decision support systems

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