Cs Online Analytical Procc

8/14/2019 Cs Online Analytical Procc

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TECHNICAL SEMINAR PRESENTATION

Murali Mohan Rath CS200117198

OnLine Analytical ProcessingSeminar Presentation

By


Under the guidance of

Mr. Indraneel Mukhopadhyay


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What is OLAP

OnLine Analytical Processing ia a

technology that uses multidimensional view

of aggregate data for quicker access tostrategic information.


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How OLAP Helps

It helps in decision making, business

modeling, and operations research activities

by transforming raw Data warehouse datainto strategic information


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Four Main Characteristics of OLAP

Use multidimensional data analysistechniques

Provide advanced database supportProvide easy-to-use end userinterfaces

Support client/server architecture


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Multidimensional Data Analysis

Techniques

The processing of data in which data

are viewed as part of a

multidimensional structure.

Multidimensional view allows end

users to consolidate or aggregate dataat different levels.


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Multidimensional Data Analysis

Relational databases contains lists of

records whose information is organised into

fields and is based on a row and column

data format(one dimensional).

However some relational tables where thereis more than a one-to-one relationshipbetween the fields lends itself tomultidiensional represtation.


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Product Region Sales

Nuts East 50

Nuts West 60

Nuts Central 100

Bolts East 90

Bolts West 120

Bolts Central 140

Screws East 40

Screws West 70Screws Central 80

Washers East 20

Washers West 10

Washers Central 30

Single Dimensional View


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East West Central

Nuts 50 60 100

Bolts 90 120 140

Screws 40 70 80

Washers 20 10 30

Multidimensional View


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OLAP Architecture

Three Main Modules

xOLAP Graphical User Interface (GUI)

xOLAP Analytical Processing Logic

xOLAP Data Processing Logic

OLAP systems are designed touse both operational and DataWarehouse data.


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OLAP Server Arrangement



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OLAP Server With Multidimensional Data Store Arrangement



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Relational OLAP

Relational On-Line Analytical Processing(ROLAP) provides OLAP functionality by usingrelational database and familiar relationalquery tools.

Extensions to RDBMS

Multidimensional data schema support within theRDBMS

Data access language and query performanceoptimized for multidimensional data

Support for very large databases



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Multidimensional Data Schema Support

within the RDBMSNormalization of tables in relational technology isseen as a stumbling block to its use in OLAPsystems.

DSS data tend to be non-normalized, duplicated,and pre-aggregated.

ROLAP uses a special design technique to enableRDBMS technology to support multidimensional data

representations, known as star schema.Star schema creates the near equivalent of amultidimensional database schema from the existingrelational database


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A Typical ROLAP Client/Server Architecture


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The real limitation of OLAP databases isalmost always the number of cells and not thenumber of dimensions.

As the number of dimensions increases thenumber of cells increases expotentially so a16 dimension database with 5 members in

each has 152 billion cells.Most OLAP servers reach their limit in cellnumbers before they hit their dimensions limit

LIMITATIONS


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CONCLUSION

In essence OLAP technology is fast, flexibledata summarisation and analysis.

OLAP servers are a superior technology for

Business Intellgence applications.

OLAP servers and relational databases canwork in harmony to create an environment

that delivers data quickly to perform theanalysis needed to make the best businessdecisions.


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