DMBI

Practical - 1
Overview of SQL Server 2008 Databases and analysis services.

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Practical - 2
Design and Create cube by identifying measures and dimensions for Star Schema, Snowflake schema and fact Constellation Schema.


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Practical - 3
Design and Create cube by identifying measures and dimensions for Design storage using storage mode MOLAP, ROLAP and HOLAP.


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Practical - 4
Create calculated member using arithmetic operators and member property of dimension member.


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Practical - 5
Create and use Excel Pivot Table report based on data cube using SQL Server 2008.


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Practical - 6
Study of WEKA tool.


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Practical - 7
Implementation of Association rule mining using WEKA tool.


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Practical - 8
Implementation of Decision Tree Classifier using WEKA tool.


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Practical - 9
Study of DB Miner Tool.


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Practical - 10
Study of HDFS.


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Study of WEKA tool

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Practical - 6 Study of WEKA tool. Download Practical Introduction: Weka is open source software under the GNU General Public License. System is developed at the University of Waikato in New Zealand.”Weka”stands for the Waikato Environment for Knowledge Analysis. Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Weka has extensive help facilities built in and comes with a comprehensive manual. Weka supports several standard  data mining  tasks, more specifically, data preprocessing,  clustering ,  classification ,  regression , visualization, and  feature selection . All of Weka's techniques are predicated on the assumption that the data is available as one
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Study of DB Miner Tool

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Practical - 9 Study of DB Miner Tool. Download Practical Introduction DBMiner, a data mining system for interactive mining of multiple-level knowledge in large  relational databases, has been developed based on our years-of-research. The system  implements a wide spectrum of data mining functions, including generalization,  characterization, discrimination, association, classification, and prediction. By incorporation  of several interesting data mining techniques, including attribute-oriented induction,  progressive deepening for mining multiple-level rules, and meta-rule guided knowledge  mining, the system provides a user-friendly, interactive data mining environment with good  performance. A data mining system, DBMiner, has been developed for interactive mining of multiple-level  knowledge in large relational databases. It is based on studies of data mining techniques and  experience in the development of an early system prototype, DBLearn. The system  implements a
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Create calculated member using arithmetic operators and member property of dimension member

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Practical - 4 Create calculated member using arithmetic operators and member property of dimension member. Download Practical Calculated members are members of a dimension or a measure group that is defined based on a combination of cube data, arithmetic operators, numbers, and functions. For example, you can create a calculated member that calculates the sum of two physical measures in the cube. Calculated member definitions are stored in cubes, but their values are calculated at query time. Follow the Steps to create new calculate measure 1.       Open Cube Designer for the Analysis Services Tutorial cube, and then click the Calculations tab. 2.       On the toolbar of the Calculations tab, click New Calculated Member. A new form appears in the Calculation Expressions pane within which you define the properties of this new calculated member. The new member also appears in the Script Organizer pane. 3.       In the Name box, change the name of the calculate
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