Massively parallel processing distributed database for business intelligence

Business Intelligence (BI) is a business management term which refers to applications and technologies which are used together, provide access to and analyze data and information about company operations. Business intelligence systems can help companies have a more comprehensive knowledge of the factors affecting their business, such as metrics on sales, production, internal operations and they can help companies to make better business decisions. Business intelligence applications and technologies can help companies to store and analyze data, such as data mining, data farming and data warehouses. Data warehouses are used to store large amounts of data. This data is often used for On-Line Analytical Processing (OLAP) where short response times are essential for on-line decision support. One of the most important requirements of a data warehouse server is the query performance. The main focus of our research is finding adequate solutions to improve query response time of typical OLAP queries and improve scalability using parallel programming technique in a distributed environment. So the target of this study is to propose a design and implementation of a data warehouse in a distributed environment, using parallel programming technique. Using distributed environment, together with parallel programming is a good choice to increase the performance and to reduce cost. To show the advantage of the proposed strategy an application on banking system is given.