In data warehousing, it is common to combine the conceptual data model and the logical data model to a single step. The steps for logical data model are indicated below: Identify all entities. Identify primary keys for all entities. Find the relationships between different entities. Find all attributes for each entity. Resolve all entity relationships that is many-to-many relationships. Normalization if required. The environment provides the infrastructure to carry out the specified process. It consists of: A refined conceptual schema, which is built from a conceptual multidimensional schema enriched with design guidelines. The source schema and the DW schema. Schema mappings, which are used to represent correspondences between the conceptual schema and the source schema. A set of design rules, which apply the schema transformations to the source schema in order to build the DW schema. A set of pre-defined schema transformations that new relations from existing ones, applying DW design techniques. A transformation which keeps the transformations that where applied, providing the mappings between source and DW schemas. Logical Design compared with Physical Design The process of logical design involves arranging data into a series of logical relationships called entities and attributes. An represents a chunk of information. In relational databases, an entity often maps to a table. An is a component of an entity that helps define the uniqueness of the entity. In relational databases, an attribute maps to a column.
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Contains a collection of visualization tools and algorithms for data analysis and predictive modeling Supports standard data mining like data preprocessing, clustering, classification, regression, visualization, and feature selection Apply a learning method to a dataset & analyze the result Use a learned to make predictions on new instances different learners to a dataset & compare results MS Excel In order to bridge the gap between the common user and the complex data mining process, Microsoft has introduced a new and efficient data mining tool ,the Microsoft SQL Server 2005 Data Mining Add-Ins for Office 2007 putting data mining within the reach of every user or desktop. The add-in can be downloaded from the following link: . The software pre-requisites for using the add-in are: Microsoft Office 2007 installed. Microsoft SQL Server 2005 or above installed. Microsoft .NET 2.0 framework or higher (for SQL server 2008 only). Microsoft PowerShell (for SQL server 2008 only) Once the add-in is installed, you can see the DATA MINING tab in the EXCEL ribbon. The tab contains different options like: Data Preparation Explore Data. Data. Partition Data. Data Modeling Classify.