Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing studies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view-index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.
Let @math and @math be two sets of materialized views and indexes, respectively, that are termed candidate and are susceptible to reduce the execution cost of a given query set @math (generally supposed representative of system workload). Let @math . Let @math be the storage space allotted by the data warehouse administrator to build objects (materialized views or indexes) from set @math . The joint materialized view and index selection problem consists in building an object configuration @math that minimizes the execution cost of @math , under storage space constraint. This NP-hard problem @cite_56 @cite_17 may be formalized as follows: @math ; @math , where @math is the disk space occupied by object @math .
@cite_17 @cite_56
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Materialized view selection is a non-trivial task. Hence, its complexity must be reduced. A judicious choice of views must be cost-driven and influenced by the workload experienced by the system. In this paper, we propose a framework for materialized view selection that exploits a data mining technique (clustering), in order to determine clusters of similar queries. We also propose a view merging algorithm that builds a set of candidate views, as well as a greedy process for selecting a set of views to materialize. This selection is based on cost models that evaluate the cost of accessing data using views and the cost of storing these views. To validate our strategy, we executed a workload of decision-support queries on a test data warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when storage space is limited. A data warehouse uses multiple materialized views to efficiently process a given set of queries. These views are accessed by read-only queries and need to be maintained after updates to base tables. Due to the space constraint and maintenance cost constraint, the materialization of all views is not possible. Therefore, a subset of views needs to be selected to be materialized. The problem is NP-hard, therefore, exhaustive search is infeasible. In this paper, we design a View Relevance Driven Selection (VRDS) algorithm based on view relevance to select views. We take into consideration the query processing cost and the view maintenance cost. Our experimental results show that our heuristic aims to minimize the total processing cost, which is the sum of query processing cost and view maintenance cost. Finally, we compare our results against a popular greedy algorithm. A data warehouse stores materialized views of aggregate data derived from a fact table in order to minimize the query response time. One of the most important decisions in designing the data warehouse is the selection of materialized views. This paper presents an algorithm which provides appropriate views to be materialized while the goal is to minimize the query response time and maintenance cost. We use a data cube lattice, frequency of queries and updates on views, and view size to select views to be materialized using greedy algorithms. In spite of the simplicity, our algorithm selects views which give us better performance than views that selected by existing algorithms. Automatically selecting an appropriate set of materialized views and indexes for SQL databases is a non-trivial task. A judicious choice must be cost-driven and influenced by the workload experienced by the system. Although there has been work in materialized view selection in the context of multidimensional (OLAP) databases, no past work has looked at the problem of building an industry-strength tool for automated selection of materialized views and indexes for SQL workloads. In this paper, we present an end-to-end solution to the problem of selecting materialized views and indexes. We describe results of extensive experimental evaluation that demonstrate the effectiveness of our techniques. Our solution is implemented as part of a tuning wizard that ships with Microsoft SQL Server 2000.