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Data Warehousing and data mining are two important technologies that are becoming more established in society today due to the September 11th attacks on the World Trade Centers. Data warehouses have been used in the private sector for years, predominantly to analyze customer-buying trends. The value of the data warehousing in both government and private industry comes from capturing an.
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Description. The International Journal of Data Warehousing and Mining (IJDWM) a featured IGI Global Core Journal Title, disseminates the latest international research findings in the areas of data management and analyzation. This journal is a forum for state-of-the-art developments, research, and current innovative activities focusing on the integration between the fields of data warehousing.
Introduction to data warehousing and data mining. Suyog Dhokpande, Hitesh raut. Abstract— The Data Warehousing supports business analysis and decision making by creating an enterprise wide integrated database of summarized, historical information.
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This paper “Business Intelligence and Data Warehousing” presents a detailed discussion on business intelligence and data warehousing. This paper will discuss.
Abstract- This paper provides an overview of Data warehousing, Data Mining, OLAP, OLTP technologies, exploring the features, new applications and the architecture of Data Warehousing and data mining. The data warehouse supports on-line analytical processing (OLAP), the functional and performance.
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Data Warehousing and Data Mining Essay. Data warehousing is a useful tool for many companies because it creates an easily accessible permanent central storage space that supports data analysis, retrieval, and reporting (Rosencrance, 2011).
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The International Journal of Data Warehousing and Mining (IJDWM) aims to publish and disseminate knowledge on an international basis in the areas of data warehousing and data mining. It is published multiple times a year, with the purpose of providing a forum for state-of-the-art developments and research, as well as current innovative activities in data warehousing and mining.
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Just a couple from an academic perspective that I think would be interesting: 1. Data Warehousing and Organizational Culture - the impact each has on the success of the other 2. The next generation of data modeling - how (or should) we move past.