This paper presents an improved genetic algorithm to solve the materialized view selection problem under query cost constraints. The algorithm dynamically changes the crossover probability and mutation probability in the process of genetic. In this way, it can not only maintain the population diversity, but also ensure the convergence of the genetic algorithm. So it effectively improves the optimization ability of genetic algorithm, thus avoiding the "evolutionary stagnation" problems. Meanwhile, the improved genetic algorithm increases the processing of invalid solution to avoid the "evolutionary stagnation" problems generated by invalid cycle, thereby the efficiency of materialized view selection is greatly improved.

An Improved Approach for Materialized View Selection Based on Genetic Algorithm

The ability to afford decision makers with both accurate and timely consolidated information as well as rapid query response times is the fundamental requirement for the success of a Data Warehouse. Selecting views to materialize for the purpose of supporting the decision making efficiently is one of the most significant decisions in designing Data Warehouse. Selecting a set of derived views to materialize which minimizes the sum of total query response time & maintenance of the selected views is defined as view selection problem. Therefore, to select an appropriate set of a view is the major target that diminishes the entire query response time and also maintains the selected views. Selecting a suitable set of views that minimizes the total cost associated with the materialized views is the key objective of data warehousing. However, these views have maintenance cost, so materialization of all views is not possible. In this paper we are taking into consideration of query frequency, query processing cost and space requirement. In order to find the frequent queries, we make use of I-mine mining techniques from which the frequently user accessible queries will be generated. Then, an appropriate set of views can be selected to materialize by minimizing the total query response time and/or the storage space along with maximizing the query frequency. These can be utilized by the users to obtain the quicker results once a set of views is materialized for the data warehouse.

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An Efficient I-MINE Algorithm for Materialized Views in a Data Warehouse Environment

View Materialization is an effective method to increase query efficiency in a data warehouse. However, one encounters the problem of space insufficiency if all possible views are materialized in advance. Reducing query time by means of selecting a proper set of materialized views with a lower cost is crucial for efficient data warehousing. In addition, the costs of data warehouse creation, query, and maintenance have to be taken into account while views are materialized. The purpose of this research is to select a proper set of materialized views under the storage and cost constraints and to help speedup the entire data warehousing process. We propose a cost model for data warehouse query and maintenance along with an efficient view selection algorithm, which uses the gain and loss indices. The main contribution of our paper is to speedup the selection process of materialized views. The second one is to reduce the total cost of data warehouse query and maintenance.

Efficient Utilization of Materialized Views in a Data Warehouse

The data warehouse is subject oriented, integrated, nonvolatile and time-varying data sets, which is used to support management decision-making. 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 materialization of all views is not possible because of the space constraint and maintenance cost constraint. Selecting a suitable set of views that minimize the total cost associated with the materialized views is the key objective of data warehousing. In this paper, first the query cost view selection problem model is proposed. Second, the methods for selecting materialized views are presented. The genetic algorithm is applied to the materialized view selection problem. But with the development of genetic process, the legal solution produced become more and more difficult. Therefore, improved algorithm has been presented in this paper. Finally, in order to test the function and efficiency of our algorithms, experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and work well in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

An Improved Algorithm for Materialized View Selection

An effective method to reduce the time of response time about query is to materialize views in data warehouse. Current strategies mainly consider the methods of the primary selection of materialized views. Due to the insufficient consideration of the dynamic refreshment, we address a new dynamic method which can wash out materialized views and add new materialized views in the set of current materialized views on the basis of the greedy algorithm.

Strategy of Selecting Materialized Views Based on Cache-updating

A Data Warehouse contains lots of materialized views over the data provided by the distributed heterogeneous databases for the purpose of efficiently implementing decision-support or OLAP queries. It is important to select the right view to materialize that answer a given set of queries. In this paper, we have addressed and designed algorithm to select a set of views to materialize in order to answer the most queries under the constraint of a given space. The algorithm presented in this paper aim at making out a minimum set of views, by which we can directly respond to as many as possible user’s query requests. We use experiments to demonstrate our approach. The results show that our algorithm works better. We implemented our algorithms and a performance study of the algorithm shows that the proposed algorithm gives a less complexity and higher speeds and feasible expandability.

Selecting materialized views in a data warehouse

Selecting views to materialize impacts on the efficiency as well as the total cost of establishing and running a data warehouse. One of the most important decisions in designing a data warehouse is selection of right views to be materialized. This problem is to select a right set of views that minimizes total query response time and the cost of view maintenance under a storage space constraint. In this paper, according to our practical application, the factor that refrains us from materializing all views in the data warehouse is not the space constraint but query response time. For queries fast answers may be required. So we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance time under the constraint of a given query response time. We call it query-cost view select problem. First, we design algorithms for query-cost view select problem, we give view node matrix in order to solve it. Second , we use experiments do demonstrate the power of our approach . The results show that our algorithm works better in practical cases. We implemented our algorithms and a performance study of the algorithms shows that the proposed algorithm delivers an optimal solution. Finally, we discuss the observed behavior of the algorithms. We also identify some important issues for future investigations.

Materialized view selection based on query cost in data warehouse

The interest in analyzing data has grown tremendously in recent years. To analyze data, a multitude of technologies is need, namely technologies from the fields of Data Warehouse, Data Mining, On-line Analytical Processing (OLAP). This paper gives a new architecture of data warehouse in CIMS according to CRGC-CIMS application engineering. The data source of this architecture comes from database of CRGC-CIMS system. The data is put in global data set by extracting, filtrating and integrating, and then the data is translated to data warehouse according information request. We have addressed two advantages of the new model in CRGC-CIMS application. In addition, a Data Warehouse contains lots of materialized views over the data provided by the distributed heterogeneous databases for the purpose of efficiently implementing decision-support, OLAP queries or data mining. It is important to select the right view to materialize that answer a given set of queries. In this paper, we also have designed algorithms for selecting a set of views to be materialized in a data warehouse in order to answer the most queries under the constraint of given space. First, we give a cost model for selecting materialized views. Then we give the algorithms that adopt gradually recursive method from bottom to top. We give description and realization of algorithms. Finally, we discuss the advantage and shortcoming of our approach and future work.

Lijuan Zhou

Papers

Selecting materialized views in a data warehouse

Materialized view selection based on query cost in data warehouse

Study and application of data mining and data warehouse in CIMS