difference between data warehouse and data mining

• Data Warehousing• OLAP• Data Mining• Further Reading

2

Enroll Nowhttps://goo.gl/QbTVal

Data WarehousingData Warehousing• OLTP (online transaction processing) systems

– range in size from megabytes to terabytes– high transaction throughput

• Decision makers require access to all data– Historical and current– 'A data warehouse is a subject-oriented, integrated, time-

variant and non-volatile collection of data in support of management’s decision-making process' (Inmon 1993)

3


BenefitsBenefits• Potential high returns on investment

– 90% of companies in 1996 reported return of investment (over 3 years) of > 40%

• Competitive advantage– Data can reveal previously unknown, unavailable and

untapped information• Increased productivity of corporate decision-makers

– Integration allows more substantive, accurate and consistent analysis

4

Typical ArchitectureTypical Architecture

5

Warehouse mgr

Loadmgr

Warehouse mgr

Querymanager

DBMS

Meta-data Highlysummarizeddata

Lightly summarizeddata

Detailed data

Mainframe operationaln/w,h/w data

DepartmentalRDBMS data

Private data

External data Archive/backup

Reporting query, appdevelopment,EIS tools

OLAP tools

Data-mining tools

Source: Connolly and Begg p1157

Data WarehousesData Warehouses• Types of Data

– Detailed– Summarised– Meta-data– Archive/Back-up

6


Information FlowsInformation Flows

7

Warehouse Mgr

Loadmgr

Warehouse mgr

Querymanager

DBMS

Meta-data Highly

summ.data

Lightlysumm.

Detailed data

Operational datasource 1

Operational datasource n

Archive/backup

Reporting query, appdevelopment,EIS tools

OLAP tools

Data-mining tools

Meta-flow

Inflow

Downflow

Upflow

Outflow

Source Connolly and Begg p1162

Information Flow ProcessesInformation Flow Processes• Five primary information flows

– Inflow - extraction, cleansing and loading of data from source systems into warehouse

– Upflow - adding value to data in warehouse through summarizing, packaging and distributing data

– Downflow - archiving and backing up data in warehouse– Outflow - making data available to end users– Metaflow - managing the metadata

8

Problems of Data WarehousingProblems of Data Warehousing1. Underestimation of resources for data loading2. Hidden problems with source systems3. Required data not captured4. Increased end-user demands5. Data homogenization6. High demand for resources7. Data ownership8. High maintenance9. Long duration projects10. Complexity of integration

9

Data Warehouse DesignData Warehouse Design• Data must be designed to allow ad-hoc queries to be

answered with acceptable performance constraints• Queries usually require access to factual data

generated by business transactions– e.g. find the average number of properties rented out with a

monthly rent greater than £700 at each branch office over the last six months

• Uses Dimensionality Modelling

10

Dimensionality ModellingDimensionality Modelling• Similar to E-R modelling but with constraints

– composed of one fact table with a composite primary key– dimension tables have a simple primary key which

corresponds exactly to one foreign key in the fact table– uses surrogate keys based on integer values– Can efficiently and easily support ad-hoc end-user queries

11


Star SchemasStar Schemas• The most common dimensional model• A fact table surrounded by dimension tables• Fact tables

– contains FK for each dimension table– large relative to dimension tables– read-only

• Dimension tables– reference data– query performance speeded up by denormalising into a

single dimension table

12

E-R Model ExampleE-R Model Example

13


Star Schema ExampleStar Schema Example

14


Other SchemasOther Schemas• Snowflake schemas

– variant of star schema– each dimension can have its own dimensions

• Starflake schemas– hybrid structure– contains mixture of (denormalised) star and

(normalised) snowflake schemas15

OLAPOLAP• Online Analytical Processing

– dynamic synthesis, analysis and consolidation of large volumes of multi-dimensional data

– normally implemented using specialized multi-dimensional DBMS

• a method of visualising and manipulating data with many inter-relationships

16

Codd’s OLAP RulesCodd’s OLAP Rules1. Multi-dimensional conceptual view2. Transparency3. Accessibility4. Consistent reporting performance5. Client-server architecture6. Generic dimensionality7. Dynamic sparse matrix handling8. Multi-user support9. Unrestricted cross-dimensional operations10. Intuitive data manipulation

17

OLAP ToolsOLAP Tools• Categorised according to architecture of underlying database

– Multi-dimensional OLAP• data typically aggregated and stored according to predicted usage• use array technology

– Relational OLAP• use of relational meta-data layer with enhanced SQL

– Managed Query Environment• deliver data direct from DBMS or MOLAP server to desktop in form

of a datacube

18

MOLAPMOLAP

19

RDBServer

Load

MOLAPserver Request

Result

PresentationLayer

Database/ApplicationLogic LayerEnroll Now

https://goo.gl/QbTVal

ROLAPROLAP

20

RDBServer

ROLAPserver Request

Result

PresentationLayer

ApplicationLogic Layer

SQLResult

DatabaseLayerEnroll Now

https://goo.gl/QbTVal

MQEMQE

21

RDBServer

Load

MOLAPserver Request

Result

SQLResult

End-usertools


Data MiningData Mining• ‘The process of extracting valid, previously unknown,

comprehensible and actionable information from large databases and using it to make crucial business decisions’

focus is to reveal information which is hidden or unexpected– patterns and relationships are identified by examining the

underlying rules and features of the data– work from data up– require large volumes of data

22

Example Data Mining ApplicationsExample Data Mining Applications

• Retail/Marketing– Identifying buying patterns of customers– Finding associations among customer demographic

characteristics– Predicting response to mailing campaigns– Market basket analysis

23


Example Data Mining ApplicationsExample Data Mining Applications• Banking

– Detecting patterns of fraudulent credit card use– Identifying loyal customers– Predicting customers likely to change their credit card

affiliation– Determining credit card spending by customer groups

24


Data Mining TechniquesData Mining Techniques• Four main techniques

– Predictive Modeling– Database Segmentation– Link Analysis– Deviation Direction

25


Data Mining TechniquesData Mining Techniques• Predictive Modelling

– using observations to form a model of the important characteristics of some phenomenon

• Techniques:– Classification– Value Prediction

26


Classification Example- Tree InductionClassification Example- Tree Induction

27

Customer renting property> 2 years

Rent property

Rent property Buy property

Customer age> 25 years?

No Yes

No Yes


Data Mining TechniquesData Mining Techniques• Database Segmentation:

– to partition a database into an unknown number of segments (or clusters) of records which share a number of properties

• Techniques:– Demographic clustering– Neural clustering

28


Segmentation: Scatterplot Segmentation: Scatterplot ExampleExample

29


Data Mining TechniquesData Mining Techniques• Link Analysis

– establish associations between individual records (or sets of records) in a database

• e.g. ‘when a customer rents property for more than two years and is more than 25 years old, then in 40% of cases, the customer will buy the property’

– Techniques• Association discovery• Sequential pattern discovery• Similar time sequence discovery

30

Data Mining TechniquesData Mining Techniques• Deviation Detection

– identify ‘outliers’, something which deviates from some known expectation or norm

– Statistics– Visualisation

31


Deviation Detection: Visualisation Deviation Detection: Visualisation ExampleExample

32

Mining and Warehousing• Data mining needs single, separate, clean, integrated, self-

consistent data source• Data warehouse well equipped:

– populated with clean, consistent data– contains multiple sources– utilises query capabilities– capability to go back to data source

33

Further Reading• Connolly and Begg, chapters 31 to 34.• W H Inmon, Building the Data Warehouse, New York, Wiley

and Sons, 1993. • Benyon-Davies P, Database Systems (2nd ed), Macmillan

Press, 2000, ch 34, 35 & 36.

34


difference between data warehouse and data mining

Education