Data warehousing and OLAP Swati Vitkar Research Scholar‚ JJT University‚ Rajasthan. Abstract: Data warehousing and on-line analytical processing (OLAP) are essential elements of decision support‚ which has increasingly become a focus of the database industry. Many commercial products and services are now available‚ and all of the principal database management system vendors now have offerings in these areas. Decision support places some rather different requirements on database technology compared
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What is the difference between Data‚ Information and Knowledge? Data‚ information and knowledge are often referred to and used to represent the same thing. However‚ each term has its own meaning. By defining what data‚ information and knowledge mean individually‚ a greater understanding can be reached. It is also important to look at how they interact with each other. Knowledge‚ by definition‚ is the theoretical or practical understanding of a subject. It is the acquisition of information through
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DATA PROVENANCE IN E-LEARNING ENVIRONMENT M.S.A. Ahsan 100022U A. Burusothman 100063U S. Paraneetharan 100369M B. Sanjith 100484K K. Sureshkumar 100527X 1. Introduction We live in an information age‚ where the volume of data processed by humans and organizations increases exponentially by grid middleware and availability of huge storage capacity. So‚ Data management comprises all the disciplines related to managing data as a valuable resource.
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Data & Knowledge Engineering Introduction Database Systems and Knowledgebase Systems share many common principles. Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKEreaches a world-wide audience of researchers‚ designers‚ managers and users. The major aim of the journal is to identify‚ investigate and analyze the underlying principles in the design and effective use of these systems.DKE achieves this aim
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Paper Creating a Data Warehouse Introduction Data warehouses are the latest buzz in the business world. Not only are they used to store data for reporting and forecasting‚ but they are part of a decision support system. There are many reasons for creating and using a data warehouse. The data warehouse will support the decisions a business needs to make‚ usually on a daily basis. The data warehouse collects data‚ consolidates the data for reporting purposes. Data warehouses are accompanied
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regression model to testing and validation dataset (output is in “LR_Output2”‚ “LR_Testscore2”‚ and “LR_ValidLiftChart2”). In testcore sheet‚ we can see the probability output we generated for each row from test data. Below shows the regression model and scoring summary. 3. a) the data of purchaser only is in “Purchasers_only” sheet b) Partition is shown in “Data_Partition2” sheet c) Multiple Linear regression output can be seen in “MLR_Output1”. Target variable is “spending”. We select every
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Data Warehousing Failures Eight studies of data warehousing failures are presented. They were written based on interviews with people who were associated with the projects. The extent of the failure varies with the organization‚ but in all cases‚ the project was at least a disappointment. Read the cases and prepare a one or two page discussion of the following: 1. What’s the scope of what can be considered a data warehousing failure? Discuss. 2. What generalizations apply across
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System Based On Web Data Mining for Personalized E-learning Jinhua Sun Department of Computer Science and Technology Xiamen University of Technology‚ XMUT Xiamen‚ China jhsun@xmut.edu.cn Yanqi Xie Department of Computer Science and Technology Xiamen University of Technology‚ XMUT Xiamen‚ China yqxie@xmut.edu.cn Abstract—In this paper‚ we introduce a web data mining solution to e-learning system to discover hidden patterns strategies from their learners and web data‚ describe a personalized
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that protects the underlying attribute values of objects subjected to clustering analysis. In doing so‚ the privacy of individuals’ data would be protected. In this age of data mining‚ we felt that working on data privacy was of paramount importance. We worked in a group of 3 on this and my role specifically was to design and implement the ‘K-means’ algorithm that clusters the data and forms the basis for the data transformation. In addition to my thesis‚ I worked on a solo project which involved
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Data Dictionary: A software module and database containing descriptions and definitions concerning the structure‚ data elements‚interrelationships‚ and other characteristics of an organization’s databases. Data Mining: A process where data in a data warehouse is identified to discover key business trends and factors. Data Modeling: A process where the relationships between data elements are identified and defined to develop data models. Data Planning: A planning and analysis function
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