Data mining and Data warehousing KITS

Data mining and Data warehousing KITS - PAPER PRESENTATION...

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PAPER PRESENTATION ON “DATAMINING AND DATAWAREHOUSING AUTHOR G. Harish Final C.S.E Ph: 9985357517 e-mail: [email protected] YERRAKOTA, YEMMIGANUR-518360 KURNOOL DISTRICT .
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INDEX ABSTRACT INTRODUCTION WHAT IS DATAMINNG? WHAT IS DATA WAREHOUSING? HOW DO DATAMINING AND DATAWARE HOUSING WORK TOGETHER? APPLICATIONS ADVANTAGES DISADVANTAGES CONCLUSION REFERENCES 2
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ABSTRACT We live in the age of information. Data is the most valuable resource of an enterprise. In today’s competitive global business environment, understanding and managing enterprise wide information is crucial for making timely decisions and responding to changing business conditions. Many companies are realizing a business advantage by leveraging one of their key assets – business Data. There is a tremendous amount of data generated by day-to-day business operational applications. In addition there is valuable data available from external sources such as market research organizations, independent surveys and quality testing labs. Studies indicate that the amount of data in a given organization doubles every 5 years. Data warehousing has emerged as an increasingly popular and powerful concept of applying information technology to turn these huge islands of data into meaningful information for better business. Data mining , the extraction of hidden predictive information from large databases is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This paper describes the practicalities and the constraints in Data mining and Data warehousing and its advancements from the earlier technologies. 3
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INTRODUCTION Data Warehousing A data warehouse can be defined as any centralized data repository which can be queried for business benefit Warehousing makes it possible to o Extract archived operational data o Overcome inconsistencies between different legacy data formats o Integrate data throughout an enterprise, regardless of location, format, or communication requirements o Incorporate additional or expert information Data Mining Data mining is not an “intelligence” tool or framework, typically drawn from an enterprise data warehouse is used to analyze and uncover information about past performance on an aggregate level. Data warehousing and business intelligence provide a method for users to anticipate future trends from analyzing past patterns in organizational data. Data mining is more intuitive, allowing for increased insight beyond data warehousing. An implementation of data mining in an organization will serve as a guide to uncover inherent trends and tendencies in historical information, as well as allow for statistical predictions, groupings and Classification of data. Typical data warehousing implementations in organizations will allow users to ask
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Data mining and Data warehousing KITS - PAPER PRESENTATION...

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