A Survey on Different Levels of Risks during Different Phases in Data Warehouse
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2016, Vol 18, Issue 3
Abstract
Abstract: The term Data Warehouse represents huge collection of historical data which are subject-oriented, non-volatile, integrated, and time-variant and such data is required for the business needs [1]. Data warehouses and on-line analytical processing (OLAP) tools have become essential elements of decision support systems. Traditionally, data warehouses are refreshed periodically (for example, nightly) by extracting, transforming, cleaning and consolidating data from several operational data sources. The data in the warehouse is then used to periodically generate reports, or to rebuild multidimensional (data cube) views of the data for on-line querying and analysis. Increasingly, business intelligence applications in telecommunications, electronic commerce, and other industries, that are characterized by very high data volumes and data flowrates, and that require continuous analysis and mining of the data. For such applications, rather different data warehousing and on-line analysis architectures are required [2]. Although Data warehousing is a part ofBusiness Intelligence and the motto of implementing a data warehouse is for business strategic needs, however our scope is to understand the various components that is involved in data warehousing, their purpose of work and scope of each components. Through this study we will be working on the methodologies of data cleansing techniques and improvement in such area [3].
Authors and Affiliations
Prangyan Mohapatra , Nachiketa Tarasia , Ananta Chandra Das
Keywords
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