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.
Classical papers in materialized view selection introduce a lattice framework that models and captures dependency (ancestor or descendent) among aggregate views in a multidimensional context @cite_34 @cite_23 @cite_0 @cite_37 . This lattice is greedily browsed with the help of cost models to select the best views to materialize. This problem has first been addressed in one data cube and then extended to multiple cubes @cite_8 . Another theoretical framework, the AND-OR view graph, may also be used to capture the relationships between views @cite_36 @cite_57 @cite_9 @cite_39 . Unfortunately, the majority of these solutions are theoretical and are not truly scalable.
@cite_37 @cite_8 @cite_36 @cite_9 @cite_39 @cite_0 @cite_57 @cite_23 @cite_34
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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. In this paper, we describe the design of a data warehousing system for an engineering company 'R'. A cost model was developed for this system to enable the evaluation of the total costs and benefits involved in selecting each materialized view. Using the cost analysis methodology for evaluation, an adapted greedy algorithm has been implemented for the selection of materialized views. The algorithm and cost model were applied to a set of real-life database items extracted from company 'R'. By selecting the most cost effective set of materialized summary views, the total of the maintenance, storage and query costs of the system is optimized, thereby resulting in an efficient data warehousing system. 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 data from one or more sources, with the purpose of efficiently implementing decision-support or OLAP queries. One of the most important decisions in designing a data warehouse is the selection of materialized views to be maintained at the warehouse. The goal is to select an appropriate set of views that minimizes total query response time and the cost of maintaining the selected views, given a limited amount of resource, e.g., materialization time, storage space, etc. In This work, we have developed a theoretical framework for the general problem of selection of views in a data warehouse. We present polynomial-time heuristics for a selection of views to optimize total query response time under a disk-space constraint, for some important special cases of the general data warehouse scenario, viz.: 1) an AND view graph, where each query view has a unique evaluation, e.g., when a multiple-query optimizer can be used to general a global evaluation plan for the queries, and 2) an OR view graph, in which any view can be computed from any one of its related views, e.g., data cubes. We present proofs showing that the algorithms are guaranteed to provide a solution that is fairly close to (within a constant factor ratio of) the optimal solution. We extend our heuristic to the general AND-OR view graphs. Finally, we address in detail the view-selection problem under the maintenance cost constraint and present provably competitive heuristics. Pre-computation and materialization of views with aggregate functions is a common technique in Data Warehouses. Due to the complex structure of the warehouse and the different profiles of the users who submit queries, there is need for tools that will automate the selection and management of the materialized data. In this paper we present DynaMat, a system that dynamically materializes information at multiple levels of granularity in order to match the demand (workload) but also takes into account the maintenance restrictions for the warehouse, such as down time to update the views and space availability. DynaMat unifies the view selection and the view maintenance problems under a single framework using a novel “goodness” measure for the materialized views. DynaMat constantly monitors incoming queries and materializes the best set of views subject to the space constraints. During updates, DynaMat reconciles the current materialized view selection and refreshes the most beneficial subset of it within a given maintenance window. We compare DynaMat against a system that is given all queries in advance and the pre-computed optimal static view selection. The comparison is made based on a new metric, the Detailed Cost Savings Ratio introduced for quantifying the benefits of view materialization against incoming queries. These experiments show that DynaMat's dynamic view selection outperforms the optimal static view selection and thus, any sub-optimal static algorithm that has appeared in the literature. The goal of on-line analytical processing (OLAP) is to quickly answer queries from large amounts of data residing in a data warehouse. Materialized view selection is an optimization problem encountered in OLAP systems. Published work on the problem of materialized view selection presents solutions scalable in the number of possible views. However, the number of possible views is exponential relative to the number of database dimensions. A truly scalable solution must be polynomial time relative to the number of dimensions. We present such a solution, our Polynomial Greedy Algorithm. Complexity analysis proves scalability, and a performance study verifies the result. Empirical evidence demonstrates benefits close to existing algorithms. We conclude the Polynomial Greedy Algorithm functions effectively where existing algorithms fail dramatically. A multidimensional database is a data repository that supports the efficient execution of complex business decision queries. Query response can be significantly improved by storing an appropriate set of materialized views. These views are selected from the multidimensional lattice whose elements represent the solution space of the problem. Several techniques have been proposed in the past to perform the selection of materialized views for databases with a reduced number of dimensions. When the number and complexity of dimensions increase, the proposed techniques do not scale well. The technique we are proposing reduces the soluticn space by considering only the relevant elements of the multidimensional lattice. An additional statistical analysis allows a further reduction of the solution space.