Abstract:
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.
Seifedine Kadry and Khaled Smaili, 2008. Massively Parallel Processing Distributed Database for Business Intelligence. Information Technology Journal, 7: 70-76.
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