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The Data Mining process is the extraction of valid and previously unknown information. Data warehouse is a subject oriented, integrated, time-variant, non-volatile collection of data in support of management decision- making process. To handle bulk of data in various enterprises, thereby increasing the margin To turn incomprehensible data into usable information It is a combination of ideas from statistics, machine learning, databases and parallel computing As it has both hardware & soft ware components, which facilitates in taking better decisions in massive companies It is also an architecture & tool to collect, query, analyze and present information 2. Discovery Driven Data Mining: 1.Verification Driven Data Mining: Querying and reporting, presenting the output in graphical, tabular and textual forms, through multi-dimensional analysis and through statistical analysis. There are four different Discovery Driven Data Mining approaches for at present: Predictive Modeling including neural nets, Link-analysis Technique which attempts to establish links between records, Database Segmentation which partitions the data into collections of related records, and Deviation Detection which identifies point that do not fit in a segment. FOUR FEATURES ARE: Subject-oriented relevant data is stored as a single set in useful format Integrated data being stored in globally acceptable fashion Non-volatile means the data warehouse is read-only Time-variant data represents long term The process of data mining is not a simple one as, Data stored in Database It needs a variety of feedback loops to produce results of high expected quality, At the core of the design of the data warehouse lies a multidimensional view of the data model. Some of them are as follows: PROFESSION ENGINEER SECRETARY TEACHER Dimension Modeling Lattice of Cuboids Data Cube COMMON SERVICES LAYER Discovery and analysis of useful information from the World Wide Web. Web content mining is the process of extracting knowledge from the content of documents or their descriptions Web usage mining, also known as Web Log Mining, is the process of extracting interesting patterns in web access logs. Data mining predict future trends and behaviors, allowing businesses to make proactive, knowledge driven decisions. Using a method called “neural segmentation”, a number of different types of purchase patterns can be identified and then customer groupings can be associated with this data. To minimize the resources, it is necessary to identify what factors affect the crop yield, out of such items as chemical fertilizers and additives. DATA MINING Sales and marketing analysis across all industries. Category management, vendor analysis, and marketing program effectiveness analysis in retail. Profitable lane or driver risk analysis in transportation. Profitability analysis or risk assessment in banking. Understand business trends and make better forecasting decisions. Category management, vendor analysis, and marketing program effectiveness analysis in retail. Claims analysis or fraud detection in insurance. DATA WAREHOUSING A good data warehousing & mining will... provide the RIGHT data... to the RIGHT people... at the RIGHT time: RIGHT NOW! While data warehouse organizes data for business analysis, Internet has emerged as the standard for information sharing. So the future of data warehousing & mining lies in their accessibility from the Internet. Convergence of the ERD, Dimensional and ETL Models makes completing the data modeling tasks Easier. UML models also need to be available in conceptual schema for certain applications. The future world would result in both convergence and standardization of data models for Data Warehousing. PLEASE CLARIFY YOUR DOUBTS TO STUN ME !!!