Data Mining and Warehousing Techniques

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Background - One of the most promising developments in the field of computing and computer memory over the past few decades has been the ability to bring tremendous complex and large data sets into database management that are both affordable and workable for many organizations. Improvement in computer power has also allowed for the field of artificial intelligence to evolve which also improves the sifting of massive amounts of information for appropriate use in business, military, governmental, and academic venues. Essentially, data mining is taking as much information as possible for a variety of databases, sifting it intelligently and coming up with usable information that will help with data prediction, customer service, what if scenarios, and extrapolating trends for population groups (Ye, 2003; Therling, 2009). . In any data warehousing model, the ultimate success of the operation is entirely dependent upon the strengths and weaknesses of the information delivery tools. If the tools are effective, data will be available in a robust manner that it ultimately appropriate for the end user. Because there are so many different types of delivery mechanisms, the data must be available in a variety of formats. In the data warehouse for instance, the data must be transactional from numerous sources and have the ability to slice and dice into usable reports. We can think of it as a major information repository functioning in three layers: staging used to store the raw data
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