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  1. #1
    Join Date
    Dec 2003
    Delhi INDIA

    Lightbulb Unanswered: Basic OLAP require !!

    Hi Champs!!

    I am totally new in OLAP and want know more prior to start....pls tell me know the difference between these..

    Regular/Virtual/Linked cubes..
    Star schema/ snow flake/ parent-child/ virtual dimension/ mining model

    Thanks in advance
    -Deepak Kumar.

    How much data you can afford to lose??

  2. #2
    Join Date
    Aug 2004

    Regarding various OLAP attributes concepts

    Cube consist of dimensions on ehich to analyse the data.

    1>Regular Cube
    A regular cube is based on tables in the databases specified in
    the data sources of the cube's partitions

    2>Virtual Cube
    created with one or more regular cubes.
    A virtual cube is a combination of multiple cubes in one logical
    cube, somewhat like a relational database view that combines other
    views and tables.Thus it contains measures from the related cubes.

    3> Linked Cube
    A linked cube is based on another cube that is defined and stored
    on another Analysis server. To end users, linked cubes appear and
    function like regular cubes. By using linked cubes, you can create,
    store, and maintain a cube on one Analysis server while the cube is
    also available as linked cubes on multiple Analysis servers

    1>Regular Dimension
    every cube must has atleast one dimension.It is the default dimension.
    dimensions have associated aggregation data in the cubes in which they
    are used. A regular dimension contains a number of levels equal to the
    number of columns selected during its definition

    2>Virtual Dimension
    Based on other dimensions.
    A virtual dimension is a logical dimension based on the columns from
    a physical dimension

    3>Parent Child Dimension
    A parent-child dimension is based on two dimension table columns that
    together define the lineage relationships among the members of the
    dimension. One column, called the member key column, identifies each
    member; the other column, called the parent key column, identifies the
    parent of each member. This information is used to create parent-child
    links, which are then combined into a single member hierarchy that
    represents a single meta data level.

    For example, in the Employee table, the column that identifies each
    member is Employee_Number. The column that identifies the parent of
    each member is Manager_Employee_Number
    **Mining Model
    A mining model enables you to analyze your data for patterns and to make
    predictions based on the patterns. You can create a mining model from a
    relational schema or a cube, and you can store output from the model in
    a tabular column, a cube dimension, or a mining model diagram.


    **Star Schema
    Star Schema: A single dimension table

    Select to create a regular dimension based on a single dimension table.
    The depth of the dimension depends on the number of levels you select
    in a later step. Each level is derived from a column.
    When the dimension is added to a cube, the dimension table joins
    to the fact table. If each of the cube's dimensions is based on a
    single table, the cube has a star schema.

    **Snowflake Schema: Multiple, related dimension tables

    Select to create a regular dimension based on multiple, joined dimension
    tables. The depth of the dimension depends on the number of levels you
    select in a later step. Each level is derived from a column.

    When the dimension is added to a cube, only one of its dimension
    tables joins to the fact table. Therefore, the cube has a snowflake schema.

    Praveen Kumar Pandey

    CoVisible Soltuions

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