Difference Between Data Warehouse And Data Mining 6yg1m
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1
• • • •
Data Warehousing OLAP Data Mining Further Reading
Enroll Now https://goo.gl/QbTVal 2
Data 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, timevariant and non-volatile collection of data in of management’s decision-making process' (Inmon 1993)
Enroll Now https://goo.gl/QbTVal 3
Benefits • 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 Architecture Mainframe operational n/w,h/w data Warehouse mgr Meta-data
Departmental RDBMS data Private data
Load mgr
Highly summarized Query data manager Lightly summarized data Detailed data DBMS Warehouse mgr
External data
Reporting query, app development,EIS tools
OLAP tools
Data-mining tools Archive/backup
Source: Connolly and Begg p1157 5
Data Warehouses • Types of Data – Detailed – Summarised – Meta-data – Archive/Back-up
Enroll Now https://goo.gl/QbTVal 6
Information Flows Operational data source 1
Inflow Load mgr
Warehouse Mgr Meta-flow Metadata Highly summ. data Lightly Upflow summ. Detailed data DBMS Warehouse mgr
Downflow
Operational data source n Source Connolly and Begg p1162
Reporting query, app development,EIS tools Outflow Query manager
OLAP tools
Data-mining tools Archive/backup
7
•
Information 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 s – Metaflow - managing the metadata 8
1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Problems of Data Warehousing Underestimation of resources for data loading Hidden problems with source systems Required data not captured Increased end- demands Data homogenization High demand for resources Data ownership High maintenance Long duration projects Complexity of integration
9
Data 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 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 Enroll Now – Can efficiently and easily ad-hoc end- https://goo.gl/QbTVal queries 11
Star 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 Example
Enroll Now https://goo.gl/QbTVal 13
Star Schema Example
Enroll Now https://goo.gl/QbTVal 14
Other 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 15
OLAP • Online Analytical Processing – dynamic synthesis, analysis and consolidation of large volumes of multi-dimensional data – normally implemented using specialized multidimensional DBMS • a method of visualising and manipulating data with many inter-relationships
16
Codd’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- 9. Unrestricted cross-dimensional operations 10. Intuitive data manipulation
17
OLAP 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
MOLAP RDB Server
MOLAP server Load
Database/Application Enroll Now Logic Layer
https://goo.gl/QbTVal
Request Result
Presentation Layer 19
ROLAP RDB Server
Database Layer Enroll Now
SQL Result
https://goo.gl/QbTVal
ROLAP server
Request Result
Application Logic Layer
Presentation Layer
20
MQE RDB Server
End- tools
SQL Result MOLAP server Load
Request Result
Enroll Now https://goo.gl/QbTVal 21
Data 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 Applications • Retail/Marketing – Identifying buying patterns of customers – Finding associations among customer demographic characteristics – Predicting response to mailing campaigns – Market basket analysis
Enroll Now https://goo.gl/QbTVal 23
Example 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
Enroll Now https://goo.gl/QbTVal
24
Data Mining Techniques • Four main techniques – Predictive Modeling – Database Segmentation – Link Analysis – Deviation Direction
Enroll Now https://goo.gl/QbTVal 25
Data Mining Techniques • Predictive Modelling – using observations to form a model of the important characteristics of some phenomenon • Techniques: – Classification – Value Prediction
Enroll Now https://goo.gl/QbTVal
26
Classification Example- Tree Induction Customer renting property No
> 2 years
Yes Customer age > 25 years?
Rent property No Rent property
Yes Buy property
Enroll Now https://goo.gl/QbTVal 27
Data 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 Enroll Now
https://goo.gl/QbTVal
28
Segmentation: Scatterplot Example
Enroll Now https://goo.gl/QbTVal 29
Data 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 Techniques • Deviation Detection – identify ‘outliers’, something which deviates from some known expectation or norm – Statistics – Visualisation
Enroll Now https://goo.gl/QbTVal
31
Deviation Detection: Visualisation Example
32
Mining and Warehousing
• Data mining needs single, separate, clean, integrated, selfconsistent 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.
Enroll Now https://goo.gl/QbTVal 34
35
36
• • • •
Data Warehousing OLAP Data Mining Further Reading
Enroll Now https://goo.gl/QbTVal 2
Data 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, timevariant and non-volatile collection of data in of management’s decision-making process' (Inmon 1993)
Enroll Now https://goo.gl/QbTVal 3
Benefits • 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 Architecture Mainframe operational n/w,h/w data Warehouse mgr Meta-data
Departmental RDBMS data Private data
Load mgr
Highly summarized Query data manager Lightly summarized data Detailed data DBMS Warehouse mgr
External data
Reporting query, app development,EIS tools
OLAP tools
Data-mining tools Archive/backup
Source: Connolly and Begg p1157 5
Data Warehouses • Types of Data – Detailed – Summarised – Meta-data – Archive/Back-up
Enroll Now https://goo.gl/QbTVal 6
Information Flows Operational data source 1
Inflow Load mgr
Warehouse Mgr Meta-flow Metadata Highly summ. data Lightly Upflow summ. Detailed data DBMS Warehouse mgr
Downflow
Operational data source n Source Connolly and Begg p1162
Reporting query, app development,EIS tools Outflow Query manager
OLAP tools
Data-mining tools Archive/backup
7
•
Information 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 s – Metaflow - managing the metadata 8
1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
Problems of Data Warehousing Underestimation of resources for data loading Hidden problems with source systems Required data not captured Increased end- demands Data homogenization High demand for resources Data ownership High maintenance Long duration projects Complexity of integration
9
Data 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 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 Enroll Now – Can efficiently and easily ad-hoc end- https://goo.gl/QbTVal queries 11
Star 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 Example
Enroll Now https://goo.gl/QbTVal 13
Star Schema Example
Enroll Now https://goo.gl/QbTVal 14
Other 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 15
OLAP • Online Analytical Processing – dynamic synthesis, analysis and consolidation of large volumes of multi-dimensional data – normally implemented using specialized multidimensional DBMS • a method of visualising and manipulating data with many inter-relationships
16
Codd’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- 9. Unrestricted cross-dimensional operations 10. Intuitive data manipulation
17
OLAP 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
MOLAP RDB Server
MOLAP server Load
Database/Application Enroll Now Logic Layer
https://goo.gl/QbTVal
Request Result
Presentation Layer 19
ROLAP RDB Server
Database Layer Enroll Now
SQL Result
https://goo.gl/QbTVal
ROLAP server
Request Result
Application Logic Layer
Presentation Layer
20
MQE RDB Server
End- tools
SQL Result MOLAP server Load
Request Result
Enroll Now https://goo.gl/QbTVal 21
Data 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 Applications • Retail/Marketing – Identifying buying patterns of customers – Finding associations among customer demographic characteristics – Predicting response to mailing campaigns – Market basket analysis
Enroll Now https://goo.gl/QbTVal 23
Example 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
Enroll Now https://goo.gl/QbTVal
24
Data Mining Techniques • Four main techniques – Predictive Modeling – Database Segmentation – Link Analysis – Deviation Direction
Enroll Now https://goo.gl/QbTVal 25
Data Mining Techniques • Predictive Modelling – using observations to form a model of the important characteristics of some phenomenon • Techniques: – Classification – Value Prediction
Enroll Now https://goo.gl/QbTVal
26
Classification Example- Tree Induction Customer renting property No
> 2 years
Yes Customer age > 25 years?
Rent property No Rent property
Yes Buy property
Enroll Now https://goo.gl/QbTVal 27
Data 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 Enroll Now
https://goo.gl/QbTVal
28
Segmentation: Scatterplot Example
Enroll Now https://goo.gl/QbTVal 29
Data 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 Techniques • Deviation Detection – identify ‘outliers’, something which deviates from some known expectation or norm – Statistics – Visualisation
Enroll Now https://goo.gl/QbTVal
31
Deviation Detection: Visualisation Example
32
Mining and Warehousing
• Data mining needs single, separate, clean, integrated, selfconsistent 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.
Enroll Now https://goo.gl/QbTVal 34
35
36
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