tailieunhanh - Database Modeling & Design Fourth Edition- P33

Database Modeling & Design Fourth Edition- P33: Database technology has evolved rapidly in the three decades since the rise and eventual dominance of relational database systems. While many specialized database systems (object-oriented, spatial, multimedia, etc.) have found substantial user communities in the science and engineering fields, relational systems remain the dominant database technology for business enterprises. | Business Intelligence Business intelligence has become a buzzword in recent years. The database tools found under the heading of business intelligence include data warehousing online analytical processing OLAP and data mining. The functionalities of these tools are complementary and interrelated. Data warehousing provides for the efficient storage maintenance and retrieval of historical data. OLAP is a service that provides quick answers to ad hoc queries against the data warehouse. Data mining algorithms find patterns in the data and report models back to the user. All three tools are related to the way data in a data warehouse are logically organized and performance is highly sensitive to the database design techniques used Barquin and Edelstein 1997 . The encompassing goal for business intelligence technologies is to provide useful information for decision support. Each of the major DBMS vendors is marketing the tools for data warehousing OLAP and data mining as business intelligence. This chapter covers each of these technologies in turn. We take a close look at the requirements for a data warehouse its basic components and principles of operation the critical issues in its design and the important logical database design elements in its environment. We then investigate the basic elements of OLAP and data mining as special query techniques applied to data warehousing. We cover data warehousing in Section OLAP in Section and data mining in Section . 147 148 CHAPTER 8 Business Intelligence Data Warehousing A data warehouse is a large repository of historical data that can be integrated for decision support. The use of a data warehouse is markedly different from the use of operational systems. Operational systems contain the data required for the day-to-day operations of an organization. This operational data tends to change quickly and constantly. The table sizes in operational systems are kept manageably small by periodically purging old data. The