محتوى المقالة الرئيسي
Over the last decade, the data warehouse contains large amounts of information, often combine from a variety of independent sources. Decision support functions in a warehouse such as on-line analytical processing (OLAP) and analysis, involve hundreds or thousands of complex aggregate queries over large volumes of data. Warehouse applications, therefore build a large number of summary tables or materialized aggregate views (stored in disk) to help improve system performance. It is not suitable for execution queries by scanning the data sets each time. The information stored at the data warehouse is in forms of the views referred to as materialized views. The design of the data warehouse is one of the core research problems in studying and evolution of the data warehouse. One of the most important decisions in the design of a data warehouse is the data warehouse selected view. The selected views to materialize effects on efficiency as well as the total cost of establishing and running a data warehouse. The main goal of this paper is to aggregate data from a different source and consolidate the data imported and summarize this data to avoid analyzing all the datasets. This helps to reduce implementation time by using two techniques to storage summaries (materialized and index views) which helps to increase query speed and application the hybrid intelligent technology (ANN and decision tree algorithm) for access to the decision support system. The proposed system is applied to graduate students where the percentage of graduate students was determined by department, gender, and age. the proposed artificial neural network and Decision tree algorithm were obtained high efficiency to process large data warehouse size with (7488052 records) at very little time that is (4 minutes and 52 seconds).