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Transcript of OLAP
* On-line Analytical processing is a technology that uses multidimensional view of aggregate data for quicker access to strategic information
*OLAP processing is often used for data mining .
* OLAP is a technology for data discovery, including abilities for limitless report viewing, and complex analytical calculations for
large collections of historical data .
- Business reporting for sales
- Financial application
Why OLAP ?
Data Ware House & OLAP
The source data for OLAP is Online Transactional Processing (OLTP) databases that are commonly stored in data warehouses.
* How do the total sales of all products for 2007 compare with the total sales from 2006?
* How does our profitability to date compare with the same time period during the past five years?
OLAP is a way of making transactional data usable and understandable for decision making.
- OLAP and business intelligence help answer the following types of questions about business data:
Why OLAP ?
* An OLAP Cube is a data structure that allows fast analysis of data and enable the OLAP to achieve the multidimensional functionality .
* Measures : is a set of values in a cube that are based on a column in the cube's fact table and that are usually numeric values.
* Dimensions : a set of one or more organized hierarchies of levels in a cube that a user understands and uses as the base for data analysis.
* Hierarchy : a logical tree structure that organizes the members of a dimension such that each member has one parent member and zero or more child members.
* Cube is the Key of OLAP
A star schema is a common organization for data at a warehouse. It consists of :
Fact Table & Dimension Table.
1. Fact table : a very large accumulation of facts such as sales. fact table contains measure which need to be analysis.
: Performs a selection of one attribute on one dimension of the given cube .
The dice operation defines a sub cube by performing a selection on two or more dimension.
To change the dimensional orientation
increase the level of aggregation a long one or more classification hierarchies (summarized data by climbing up hierarchies)
reverse of roll up (… moving from higher level summary to lower level summary or detailed data by introducing new dimensions)
Now , let's solve this question :D
Q : 1- find the US slice in 2007 .
2- find the drill down of games slice .
3- draw the dice of Los Angeles, New York, games, books, 2007 and 2006.
4- draw the dice of books, CDs, DVDs, 2007 & 2006 in Canadaز
Suppose that :
* games is divided into two equal sub products :
1- children games .
2- adult games .
Example on Time Hierarchy :
2- SnowFlake Schema
* Los Angeles & New York are cities in US .
* Toronto & Montreal are cities in Canada .
2. Dimension tables : smaller, generally static information about the entities involved in the fact.
* The Dimension table contains the attributes based on which you need to summarize or analyze the data.
* Example : Product Dimension.
- it contains textual, descriptive information.
- it doesn't contain measurable information.
* in Star Schema : -
- each dimension is represented by one dimension table.
- each dimension table is linked to a fact table.