Data Warehousing and Olap
Data warehousing and OLAP
Swati Vitkar
Research Scholar, JJT University, Rajasthan.
Abstract:
Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse.
1. Introduction
In the 1990s, as businesses grew more complex, corporation spread globally, and competition became fiercer, business executives became desperate for information to stay competitive and improve the bottom line. Data warehousing technologies have been successfully deployed in many industries: manufacturing (for order shipment and customer support), retail (for user profiling and inventory management), financial services (for claims analysis, risk analysis, credit card analysis, and fraud detection), transportation (for fleet management), telecommunications (for call analysis and fraud detection), utilities (for power usage analysis), and healthcare (for outcomes analysis). This paper presents a roadmap of data warehousing technologies, focusing on the special requirements that data warehouses place on database management systems (DBMSs).
A data warehouse is a “subject-oriented, integrated, time varying, non-volatile collection of data that is used primarily in organizational
Swati Vitkar
Research Scholar, JJT University, Rajasthan.
Abstract:
Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Many commercial products and services are now available, and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared to traditional on-line transaction processing applications. This paper provides an overview of data warehousing and OLAP technologies, with an emphasis on their new requirements. We describe back end tools for extracting, cleaning and loading data into a data warehouse; multidimensional data models typical of OLAP; front end client tools for querying and data analysis; server extensions for efficient query processing; and tools for metadata management and for managing the warehouse.
1. Introduction
In the 1990s, as businesses grew more complex, corporation spread globally, and competition became fiercer, business executives became desperate for information to stay competitive and improve the bottom line. Data warehousing technologies have been successfully deployed in many industries: manufacturing (for order shipment and customer support), retail (for user profiling and inventory management), financial services (for claims analysis, risk analysis, credit card analysis, and fraud detection), transportation (for fleet management), telecommunications (for call analysis and fraud detection), utilities (for power usage analysis), and healthcare (for outcomes analysis). This paper presents a roadmap of data warehousing technologies, focusing on the special requirements that data warehouses place on database management systems (DBMSs).
A data warehouse is a “subject-oriented, integrated, time varying, non-volatile collection of data that is used primarily in organizational
References: 3 Codd, E.F., S.B. Codd, C.T. Salley, “Providing OLAP (On-Line Analytical Processing) to User Analyst: An IT Mandate.” Available from Arbor Software’s web site 5 Kimball, R. The Data Warehouse Toolkit. John Wiley, 1996. 6 Wu, M-C., A.P. Buchmann. “Research Issues in Data Warehousing.” Submitted for publication7 Blakeley, J.A., N. Coburn, P. Larson. “Updating