OLAP  & DSS  SUPPORT IN DATA  WAREHOUSE
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OLAP & DSS SUPPORT IN DATA WAREHOUSE

By -. . Pooja. . Sinha. . Kaushalya. . Bakde. Data Warehouse. . “A data warehouse is a . subject-oriented. , . integrated. , . time-variant. , and . nonvolatile. collection of data in support of management’s decision-making process.”—W. H. Inmon.

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OLAP & DSS SUPPORT IN DATA WAREHOUSE




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Presentation on theme: "OLAP & DSS SUPPORT IN DATA WAREHOUSE"— Presentation transcript:

Slide1

OLAP & DSS SUPPORT IN DATA WAREHOUSE

By -

Pooja

Sinha

Kaushalya

Bakde

Slide2

Data Warehouse

“A data warehouse is a

subject-oriented

,

integrated

,

time-variant

, and

nonvolatile

collection of data in support of management’s decision-making process.”—W. H. Inmon

decision support system

 (

DSS

) is a computer program application that analyzes business data and presents it so that users can make business decisions more easily.

A Data Warehouse is used for

On-Line-Analytical-Processing

: “Class of tools that enables the user to gain insight into data through interactive access to a wide variety of possible views of the information”

Slide3

Understanding the term Data Warehousing

Subject Oriented:

Data that gives information about a particular subject instead of about a company's ongoing operations.

Integrated:

Data that is gathered into the data warehouse from a variety of sources and merged into a coherent whole.

Time-variant:

All data in the data warehouse is identified with a particular time period. It keeps historical data.

Non-volatile

Data is stable in a data warehouse. More data is added but data is never removed. This enables management to gain a consistent picture of the business.

Slide4

Data Warehouse for Decision Support

A data base is a collection of data organized by a database management system.

A data warehouse is a

read-only analytical database

used for a decision support system operation.

A data warehouse for decision support is often taking data from various platforms, databases, and files as source data.

The use of advanced tools and specialized technologies may be necessary in the development of decision support systems, which affects tasks, deliverables, training, and project timelines.

Slide5

Decision Support System in datawarehouse

Information Sources

Data Warehouse Server(Tier 1)

OLAP Servers(Tier 2)

Clients(Tier 3)

Operational

DB’s

Semistructured

Sources

extract

transform

load

refresh

etc.

Data Marts

Data

Warehouse

e.g., MOLAP

e.g., ROLAP

serve

OLAP

Query/Reporting

Data Mining

serve

serve

Slide6

Characteristics Of DSS

DSS should give

well structured

information.

DSS attempts to combine the use of models or

analytic techniques

with traditional data

access and retrieval functions

DSS specifically focuses on features which make them

easy to use by non computer people

in an interactive mode

DSS

emphasizes flexibility

and 

adaptability

to accommodate changes in the environment and the decision making approach of the user.

Slide7

Application Area

Slide8

OLAP, Online Analytical Processing, is capable of providing highest level of functionality and support for decision which is linked for analyzing large collections of historical data. The functionality of an OLAP tool is purely based on the existing / current data. DSS, Decision Support System, helps in taking decisions for top executive professionals. Data accessing, time-series data manipulation of an enterprise’s internal / some times external data is emphasized by DSS. The manipulation is done by tailor made tools that are task specific and operators and general tools for providing additional functionality.

OLAP and DSS

Slide9

Introduction to OLAP

OLAP(Online Analytical Processing )is computer processing that enables user to easily & selectively extract & view data from different points of view.

OLAP data is stored in multidimensional databases.

Present in

Tier II

in Data Warehouse architecture.

Slide10

Data warehouse for On Line Analytical Processing (OLAP) features

Complex queries that access millions of records.

Contains

historical data for analysis.

Provides summarized and multidimensional view of data.

Database size : 100 GB -TB

Fast response time for interactive queries.

Navigation in & out of details(drill down & roll up, slice & dice or rotation).

Ability to perform complicate calculations.

Slide11

Types Of OLAP Servers

Relational OLAP(ROLAP)

:-

ROLAP servers are placed between relational back-end server and client front-end tools.

Data is stored in tables in relational database or extended-relational database.

They use RDBMs to manage the warehouse data.

Multidimensional OLAP(MOLAP)

:-

It stores data in an optimized multi- dimensional array rather than relational database.

Fast indexing to pre-computed aggregations.

Hybrid OLAP(HOLAP)

:-

Hybrid OLAP is a combination of both ROLAP and MOLAP. It offers higher scalability of ROLAP and faster computation of MOLAP.

HOLAP servers allow to store large data volumes of detailed information. The aggregations are stored separately in MOLAP store.

Slide12

The list of OLAP operations:

Roll-up

Drill-down

Slice and dice

Pivot (rotate)

Slide13

Common OLAP Operations

1.Roll-up: Move up the hierarchy By dimension reduction.When roll-up is performed, one or more dimensions from the data cube are removed. E.g. Given total sales by city, we can roll-up to get sales by state or by country.

Slide14

OLAP Operations

2.Drill-down: Move down the hierarchyBy introducing a new dimension Lowest level can be the detail records (drill-through)It navigates the data from less detailed data to highly detailed data. E.g., Given total sales by state, can drill-down to get total sales by city.

Slide15

Contd...

3. Slice & Dice :- Select and Project on one or more dimensions. The user can view the data from many angles.The slice operation selects one particular dimension from a given cubeDice selects two or more dimensions from a given cube and provides a new sub-cube.

product

customers

store

customer

= “Smith”

Slide16

4

. Pivot(Rotate):- Changing the dimensions. It rotates the data axes in view in order to provide an alternative presentation of data

Contd...

Slide17

Applications Of OLAP

Business reporting for sales & Marketing

Management reporting

Financial Service industry (insurance, banks, etc).

Slide18

Thank You