TSGV: a table-like structure-based greedy method for materialized view selection in data warehouses
TLDR
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.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.read more
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References
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Proceedings ArticleDOI
Implementing data cubes efficiently
TL;DR: In this article, a lattice framework is used to express dependencies among views and greedy algorithms are presented to determine a good set of views to materialize, with a small constant factor of optimal.
Proceedings Article
Automated Selection of Materialized Views and Indexes in SQL Databases
TL;DR: This paper presents an end-to-end solution to the problem of selecting materialized views and indexes for SQL databases, and describes results of extensive experimental evaluation that demonstrate the effectiveness of the techniques.
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Algorithms for Materialized View Design in Data Warehousing Environment
TL;DR: A heuristic algorithm is developed which can provide a feasible solution based on individual optimal query plans and map the materialized view design problem as O-l integer programming problem, whose solution can guarantee an optimal solution.
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TL;DR: This paper proposes three cost-based heuristic algorithms: Volcano-SH and Volcano-RU, which are based on simple modifications to the Volcano search strategy, and a greedy heuristic that incorporates novel optimizations that improve efficiency greatly.
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Materialized View Selection and Maintenance Using Multi-Query Optimization
TL;DR: In this article, the authors propose an efficient view maintenance approach by exploiting common subexpressions between different view maintenance expressions, which can be materialized temporarily during view maintenance and then chosen to be maintained permanently.