Block 4 –Database life cycle - WebsDatabase life cycle OLAP 8.2 Multidimensional Data Model -...

Block 4 – Database life cycle

Th i ht tiThere are eight sections.

Section 1 IntroductionSection 1. IntroductionSection 2. Case Study Section 3 Data Anal sisSection 3. Data AnalysisSection 4. Database Design

i l iSection 5. ImplementationSection 6. Database MaintenanceSection 7. Distributed data management

Section 8. Data Warehousing

OUHK S359 Relational Databases: theory and practice T10 - 1

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Database life cycle

8 Data Warehousing

Th f ll i ill b di d- The following areas will be discussed:

D i i S t S t (DSS ) Decision Support Systems (DSSs)

Multidimensional Data Model – OLAP

Data Warehouses and Data Warehousing


Database life cycle

D i i S S (DSS )

8 Data Warehousing

Decision Support Systems (DSSs)

- are computer-based systems that incorporate data- are computer-based systems that incorporate data warehouses and employ data mining techniques to facilitate and improve strategic decision making byfacilitate and improve strategic decision making by providing decision makers with relevant informationinformation.


Database life cycle

M l idi i l D d l

8 Data Warehousing

Multidimensional Data model

- It is a conceptual data model that enables decision- It is a conceptual data model that enables decision makers to view data from different, and multiple, perspectivesperspectives.

- A multidimensional view of data allows decisionA multidimensional view of data allows decision makers to consolidate or aggregate the data collected from operational databases at differentcollected from operational databases at different levels of detail.


Database life cycle

D W h d D h i

8 Data Warehousing

Data Warehouse and Data warehousing

- It is a repository of an organisation’s operational- It is a repository of an organisation s operational data where the data is organised around the subjects of interest to the organisation such assubjects of interest to the organisation, such as customers, products, locations and sales.

- It focuses on the modelling and analysis of data for decision makers.

- Data warehousing is the process of building, managing and using data warehouses


managing and using data warehouses.

Database life cycle

D Mi i

8 Data Warehousing

Data Mining

- It is the process of finding significant previously- It is the process of finding significant, previously unknown, and potentially valuable knowledge hidden in datahidden in data.

- Data mining provides the ability to automate the identification and extraction of information from an organisation’s data warehouses, which can be used to make informed business decisions.


Database life cycle

DSSs are primaril sed for strategic decisions

8.1 Decision Support Systems

- DSSs are primarily used for strategic decisions faced by senior management, and are implemented separately from operational systemsseparately from operational systems.

OLAP (OnLine Analytical Processing systems)( y g y )- OLAP is an element of Decision Support Systems- Strategic decision making requires a system thatStrategic decision making requires a system that

facilitates the decision support tasks of an organisationorganisation.

- Query throughput and response times are the key performance metrics


performance metrics.

Database life cycle

OLTP

8.1 Decision Support Systems

OLTP- Operational procedures require a system that

automates clerical data processing tasks that are the day-to-day operations of an organisation.

- These systems are known as OnLine Transaction Processing (OLTP) systemsProcessing (OLTP) systems.

- It processes raw current data.


Database life cycle

8.2 Multidimensional Data Model - OLAP

OLAPOLAP: - is a category of software technology that enables

analysts and decision makers to gain an insight into decision support data by allowing them to view and analyse data across multiple dimensions.

- provides access to historical and summarised data, which are derived from an organisation’s operational data, often consolidated from the many applications the organisation may use.

- The data is typically stored in a data warehouse


- The data is typically stored in a data warehouse.

Database life cycle

OLAP


OLAP: - merge multidimensional data analysis tools and

database technology into an integrated client–server system. The architecture for OLAP systems:


Database life cycle

OLAP


OLAP:

- OLAP clients (presentation layer) provide tools forOLAP clients (presentation layer) provide tools for multidimensional data analysis, reporting, visualisation and data mining, andvisualisation and data mining, and

- also provide interfaces to other software papplications, such as spreadsheets and statistical analysis software packages. y p g


Database life cycle

OLAP


OLAP:

- An OLAP server (processing layer) provides anAn OLAP server (processing layer) provides an interface between OLAP clients and the DBMS (storage layer) that is used to manage the database.(storage layer) that is used to manage the database.

- An OLAP server satisfies requests from OLAP qclients by fetching data from the database at the required level of aggregation, and passing it to the q gg g , p gOLAP clients as a multidimensional array.


Database life cycle


- There are two different approaches to implement the model OLAP depending on the nature of thethe model - OLAP, depending on the nature of the underlying database technology used to store the data:data:

- relational OLAP (ROLAP), and multidimensional OLAP (MOLAP).


Database life cycle


- ROLAP employ a relational database system to store the data as fact tables and dimension tables.

MOLAP implement the m ltidimensional data- MOLAP implement the multidimensional data model directly using a multidimensional database system a database system where data issystem, a database system where data is conceptually stored in cells of a multidimensional arrayarray.


Database life cycle

8.2 Multidimensional Data Model - ROLAP

ROLAPROLAP:

- In ROLAP systems, the structural part of theIn ROLAP systems, the structural part of the multidimensional model is represented by a special database schema diagram known as a star schema.database schema diagram known as a star schema.

- Star Schema: an n-dimensional view is represented f bl d di i blas a fact table, and n dimension tables.

The fact table is at the centre of the ‘star’, whose ‘points’ are the dimension tables, which have one-to-many relationships with the fact table.


Database life cycle

ROLAP S h


ROLAP: Star schema


Database life cycle

ROLAP S fl k h


ROLAP: Snowflake schema - A variant of the star schema, called a snowflake

schema, represents concept hierarchies using several dimension tables, one table for each level of the concept hierarchy.


Database life cycle


ROLAP: - Structural Part and Manipulative Part

Structural part:

- defines the underlying data structure on which the model is based, and

- is based on facts and dimensions, each fact depending on a set of dimensions which providesdepending on a set of dimensions, which provides the context for the fact.


Database life cycle


ROLAP: - Structural Part and Manipulative Part

Manipulative part:

- defines the operators which can act on the data structure, and

- is concerned with the operators that present different perspectives of the data and performdifferent perspectives of the data and perform statistical summarization over the dimensions.


Database life cycle

S


Structure part:

FactsFacts- Numerical measures of the subject area of interest.- It depends on a set of dimensions, which provides t depe ds o a set o d e s o s, w c p ov des

a context for the measure.

Di iDimension - The perspectives by which an organisation wishes

t i th f tto view these facts.


Database life cycle

S


Structure part:

Dimensions are used for two purposes:Dimensions are used for two purposes:

- selection of data and grouping of data at the desired level of detail.

- if the dimensions are product, location and time, aif the dimensions are product, location and time, a fact could be the total sales for a particular product, in a particular region where stores are located, overin a particular region where stores are located, over a specified time period.


Database life cycle

M i l i


Manipulative part:Roll-up: - The roll-up (or drill-up) operation performs an

aggregation on a multidimensional view of data, either by ascending a concept hierarchy for a dimension, or by reducing the number of dimensions.

- The result of the operation is to produce summary- The result of the operation is to produce summary data from detailed data along one or more dimensions


dimensions.

Database life cycle

M i l i


Manipulative part:Roll-up by Time


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M i l i


Manipulative part:Roll-up by Location


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M i l i


Manipulative part:Drill-down: - is the reverse of the roll-up operation.

to move to a more detailed view of the data along- to move to a more detailed view of the data along one or more dimensions.

- performs a distribution on a multidimensional view of data, either by descending a concept hierarchy for a dimension increasing the number of dimensions


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Database life cycle

M i l i


Manipulative part:Drill-down by descending a concept hierarchy for a dimension


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M i l i


Manipulative part:Drill-down by increasing the number of dimensions


Database life cycle

M i l i


Manipulative part:Cube: - takes an n-dimensional view of data and produces the other

2 n - 1 different views, or aggregations, of the same data.


Database life cycle

8.3 Data Warehouses

- Data Warehouse as a component of a decision support system. suppo sys e .

- It facilitates decision support by providing a it f lid t d hi t i l d t ithrepository of consolidated historical data with a

logical structure that enables decision makers to i d l th d t i diff t dview and analyse the data in different, and

multiple, dimensions.


Database life cycle

8.3 Data Warehouses

- A data warehouse may be characterised as a database that is da abase a s

subject-oriented i d integrated time-variant non-volatile


Database life cycle

8.3 Data Warehouses


Database life cycle

8.3 Data Warehouses


Database life cycle

8.3 Data Warehouses

- There are three types of data warehouse that an organisation may choose to adopt to maintain its g ydecision support data:

the enterprise data warehouse the enterprise data warehouse the data mart h i l d h the virtual data warehouse


Database life cycle

8.3 Data Warehouses

E i d hEnterprise data warehouse

- is a collection of information about all subjects ofis a collection of information about all subjects of interest to an organisation. It is constructed by integrating all the relevant data from anintegrating all the relevant data from an organisation’s operational information systems.

I i ll i d il d ( ) d ll- It typically contains detailed (raw) data as well as summarised (aggregated) data, and can range in i f f i b t t t b tsize from a few gigabytes to many terabytes.


Database life cycle

8.3 Data Warehouses

E i d hEnterprise data warehouse

- It requires extensive business modelling in itsIt requires extensive business modelling in its design and may take months or even years to build.

- There are significant long term benefits to the organisation in the enhancement of its ability to make strategic decisions using the data warehouse.


Database life cycle

8.3 Data Warehouses

D MData Mart

- is a collection of information that is of value to ais a collection of information that is of value to a specific group of users within an organisation, or to support specific products. The scope is confined tosupport specific products. The scope is confined to specific selected subject(s) of interest.


Database life cycle

8.3 Data Warehouses

Vi l D W hVirtual Data Warehouse- In a virtual data warehouse, decision makers have

direct access to operational information sources through a collection of views over the operational data that are appropriate for decision support purposes. There is no separate database.

(Note: Enterprise data warehouses and data marts are examples of a physical data warehouse sinceare examples of a physical data warehouse since the data extracted from operational information sources is stored in a separate database )


sources is stored in a separate database.)

Database life cycle

8.3 Data Warehouses

A hi f D W hArchitecture of Data Warehouse

Physical data warehousing system must support thePhysical data warehousing system must support the integration of data from multiple heterogeneous distributed information sources to form a repositorydistributed information sources to form a repository of consolidated historical data.

This database should be capable of being analysed and viewed in different, and multiple, dimensions , p ,by decision makers.


Database life cycle

8.3 Data Warehouses


- The architecture also provides tools for:The architecture also provides tools for: Extracting and translating the operational data

from the information sources (extractionfrom the information sources (extraction component);

Cleaning; Cleaning; Integrating this data (integration component); L di th i t t d d t i t th h Loading the integrated data into the warehouse

database;


-

Database life cycle

8.3 Data Warehouses


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8.3 Data Warehouses



Database life cycle

8.3 Data Warehouses



Database life cycle

8.3 Data Warehouses



Database life cycle

8.3 Data Warehouses



Database life cycle

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Database life cycle

8.3 Data Warehouses


Warehouse databaseWarehouse database

- Once the data from the information sources has b d l d d i d h dbeen extracted, cleaned and integrated, the data is loaded into the warehouse database.


Database life cycle

8 Data Warehousing


Database life cycle

8 Summary

Decision Support System

Data Mining Data WarehouseData Mining Data Warehouse

MultiDimension Data ModelMultiDimension Data Modelanalysis tools

MOLAP ROLAP

• Star schema• Snowflake schema

• Roll up• Drill down

C b


• Cube

Database life cycle

~ END ~ END


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