Difference between data warehouse and data mining

What exactly is a Data Warehouse?
Termed as a special type of database, a Data Warehouse is used for storing large amounts of data, such as analytics, historical, or customer data, which can be leveraged to build large reports and also ensure data mining against it.@ http://maxonlinetraining.com/why-is-data-warehousing-online-training-important/

What is Data mining?
The process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions’
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2. •Data Warehousing
•OLAP
•Data Mining
•Further Reading
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3. 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)
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4. 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

5. Typical ArchitectureTypical Architecture
5
Warehouse mgr
Load
mgr
Warehouse mgr
Query
manager
DBMS
Meta-data Highly
summarized
data
Lightly summarized
data
Detailed data
Mainframe operational
n/w,h/w data
Departmental
RDBMS data
Private data
External data
Archive/backup
Reporting query, app
development,EIS tools
OLAP tools
Data-mining tools
Source: Connolly and Begg p1157

6. Data WarehousesData Warehouses
•Types of Data
–Detailed
–Summarised
–Meta-data
–Archive/Back-up
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7. Information FlowsInformation Flows
7
Warehouse Mgr
Load
mgr
Warehouse mgr
Query
manager
DBMS
Meta-
data
Highly
summ.
data
Lightly
summ.
Detailed data
Operational data
source 1
Operational data
source n
Archive/backup
Reporting query, app
development,EIS tools
OLAP tools
Data-mining tools
Meta-flow
Inflow
Downflow
Upflow
Outflow
Source Connolly and Begg p1162

8. 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
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9. Problems of Data WarehousingProblems of Data Warehousing
1.Underestimation of resources for data loading
2.Hidden problems with source systems
3.Required data not captured
4.Increased end-user demands
5.Data homogenization
6.High demand for resources
7.Data ownership
8.High maintenance
9.Long duration projects
10.Complexity of integration
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10. 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
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11. 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#'
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12. 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
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13. E-R Model ExampleE-R Model Example
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14. Star Schema ExampleStar Schema Example
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15. 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 schemas
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16. 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
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17. Codd’s OLAP RulesCodd’s OLAP Rules
1. Multi-dimensional conceptual view
2. Transparency
3. Accessibility
4. Consistent reporting performance
5. Client-server architecture
6. Generic dimensionality
7. Dynamic sparse matrix handling
8. Multi-user support
9. Unrestricted cross-dimensional operations
10. Intuitive data manipulation
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18. 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
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19. MOLAPMOLAP
19
RDB
Server
Load
MOLAP
server
Request
Result
Presentation
Layer
Database/Application
Logic Layer•–
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20. ROLAPROLAP
20
RDB
Server
ROLAP
server
Request
Result
Presentation
Layer
Application
Logic Layer
SQL
Result
Database
Layer•–
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21. MQEMQE
21
RDB
Server
Load
MOLAP
server
Request
Result
SQL
Result
End-user
tools•–

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22. 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
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23. 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
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24. 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
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25. Data Mining TechniquesData Mining Techniques
•Four main techniques
–Predictive Modeling
–Database Segmentation
–Link Analysis
–Deviation Direction
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26. Data Mining TechniquesData Mining Techniques
•Predictive Modelling
–using observations to form a model of the important
characteristics of some phenomenon
•Techniques:
–Classification
–Value Prediction
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27. 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•–

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28. 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
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29. Segmentation: Scatterplot Segmentation: Scatterplot
ExampleExample
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30. 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
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31. Data Mining TechniquesData Mining Techniques
•Deviation Detection
–identify ‘outliers’, something which deviates from some
known expectation or norm
–Statistics
–Visualisation
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32. Deviation Detection: Visualisation Deviation Detection: Visualisation
ExampleExample
32

33. 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

34. 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 (2
nd
ed), Macmillan Press,
2000, ch 34, 35 & 36.
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