1. Data Analytics, Data Mining, OLAP, Reporting Systems
Database Principles Constructed by Hanh Pham based on slides from: “Database Processing, Fundamentals, Design, and Implementation”, D. Kroenke, D. Auer, Prentice Hall “Database Principles: Fundamentals of Design, Implementation, and Management”, C. Coronel, S. Morris, P.Rob
Data Analytics, Data Mining, OLAP, Reporting Systems

2. Outlines data analytics and predictive analytics data mining
online analytical processing (OLAP)
How SQL extensions are used to support OLAP-type data manipulations
Reporting Systems

3. Outlines
data analytics and predictive analytics

4. The Need for Data Analysis
Managers track daily transactions to evaluate how the business is performing
Strategies should be developed to meet organizational goals using operational databases
Data analysis provides information about short-term tactical evaluations and strategies

5. Data Analytics Subset of BI functionality
Encompasses a wide range of mathematical, statistical, and modeling techniques
Purpose of extracting knowledge from data
Tools can be grouped into two separate areas:
Explanatory analytics
Predictive analytics

6. Predictive Analytics
Employs mathematical and statistical algorithms, neural networks, artificial intelligence, and other advanced modeling tools
Create actionable predictive models based on available data
Models are used in areas such as:
Customer relationships, customer service, customer retention, fraud detection, targeted marketing, and optimized pricing

7. Outlines
data mining

8. Data Mining Data-mining tools do the following: Runs in two modes
Analyze data
Uncover problems or opportunities hidden in data relationships
Form computer models based on their findings
Use models to predict business behavior
Runs in two modes
Guided
Automated

10. Data Mining Applications: The Convergence of the Disciplines

11. Data Mining Applications
Data mining applications use sophisticated statistical and mathematical techniques to find patterns and relationships that can be used to classify and predict.
Unsupervised data mining—statistical techniques are used to identify groups of entities with similar characteristics.
Cluster Analysis
Supervised data mining:
A model is developed.
Statistical techniques are used to estimate parameter values of the model.
Regression analysis

12. Cluster Analysis I

13. Cluster Analysis II

14. Cluster Analysis III

15. Data Mining Applications: Popular Data Mining Techniques
Decision tree analysis—classifies entities into groups based on past history
Logistic regression—produces equations that offer probabilities that certain events will occur
Neural Networks—complex statistical prediction techniques
Market Basket Analysis—determines patterns of associated buying behavior

16. Data Mining Applications: Market Basket Analysis
Support—the probability that two items will be purchased together
Confidence—the probability that an item will be purchased given the fact that the customer has already purchased another particular item
Lift—the ration of confidence to the basic probability that a particular item will be purchased

17. Data Mining Applications: Market Basket Analysis

18. Data Mining Applications: SQL for Market Basket Analysis
CREATE VIEW TwoItemBasket AS
SELECT T1.ItemID as FirstItem, T2.ItemID as SecondIem
FROM TRANS_DATA T1 JOIN TRANS_DATA T2
ON T1.TransactionID = T2.TransactionID
AND T1.ItemID <> T2.ItemID;
CREATE VIEW ItemSupport AS
SELECT FirstItem, SecondItem, Count(*) AS SupportCount
FROM TwoItemBasket
GROUP BY FirstItem, SecondItem;

19. Outlines
online analytical processing (OLAP)

20. Online Analytical Processing
Three main characteristics:
Multidimensional data analysis techniques
Advanced database support
Easy-to-use end-user interfaces

21. Multidimensional Data Analysis Techniques
Data are processed and viewed as part of a multidimensional structure
Augmented by the following functions:
Advanced data presentation functions
Advanced data aggregation, consolidation, and classification functions
Advanced computational functions
Advanced data modeling functions

22. Advanced Database Support
Advanced data access features include:
Access to many different kinds of DBMSs, flat files, and internal and external data sources
Access to aggregated data warehouse data
Advanced data navigation
Rapid and consistent query response times
Maps end-user requests to appropriate data source and to proper data access language
Support for very large databases

23. Easy-to-Use End-User Interface
Advanced OLAP features are more useful when access is simple
Many interface features are “borrowed” from previous generations of data analysis tools
Already familiar to end users
Makes OLAP easily accepted and readily used

24. OLAP Architecture Three main architectural components:
Graphical user interface (GUI)
Analytical processing logic
Data-processing logic

25. OLAP Architecture (cont’d.)
Designed to use both operational and data warehouse data
In most implementations, data warehouse and OLAP are interrelated and complementary
OLAP systems merge data warehouse and data mart approaches

27. Relational OLAP
Relational online analytical processing (ROLAP) provides the following extensions:
Multidimensional data schema support within the RDBMS
Data access language and query performance optimized for multidimensional data
Support for very large databases (VLDBs)

28. Multidimensional OLAP
Multidimensional online analytical processing (MOLAP) extends OLAP functionality to multidimensional database management systems (MDBMSs)
MDBMS end users visualize stored data as a 3D data cube
Data cubes can grow to n dimensions, becoming hypercubes
To speed access, data cubes are held in memory in a cube cache

29. Relational vs. Multidimensional OLAP
Selection of one or the other depends on evaluator’s vantage point
Proper evaluation must include supported hardware, compatibility with DBMS, etc.
ROLAP and MOLAP vendors working toward integration within unified framework
Relational databases use star schema design to handle multidimensional data

31. Outlines
How SQL extensions are used to support OLAP-type data manipulations

32. SQL Extensions for OLAP
Proliferation of OLAP tools fostered development of SQL extensions
Many innovations have become part of standard SQL
All SQL commands will work in data warehouse as expected
Most queries include many data groupings and aggregations over multiple columns

33. The ROLLUP Extension
Used with GROUP BY clause to generate aggregates by different dimensions
GROUP BY generates only one aggregate for each new value combination of attributes
ROLLUP extension enables subtotal for each column listed except for the last one
Last column gets grand total
Order of column list important

34. The CUBE Extension
CUBE extension used with GROUP BY clause to generate aggregates by listed columns
Includes the last column
Enables subtotal for each column in addition to grand total for last column
Useful when you want to compute all possible subtotals within groupings
Cross-tabulations are good candidates for application of CUBE extension

35. Materialized Views
A dynamic table that contains SQL query command to generate rows
Also contains the actual rows
Created the first time query is run and summary rows are stored in table
Automatically updated when base tables are updated

36. Outlines
Reporting Systems

37. Reporting Systems: Components of a Reporting System

38. Reporting Systems: RFM Analysis
RFM Analysis analyzes and ranks customers according to purchasing patterns
R = recent (most recent order)
F = frequent (how often an order is made)
M = money (dollar amount of orders)
Customers are sorted into five groups, each containing 20% of the customers.
Each group is given a numerical value:
1 = top 20%
2, 3, 4 = each 20% in between top and bottom 20%
5 = bottom 20%

39. Reporting Systems: RFM Analysis

40. Reporting Systems: Producing the RFM Analysis—Tables I

41. Reporting Systems: Producing the RFM Analysis—Tables II

42. Reporting Systems: Producing the RFM Analysis: Stored Procedure Calculate_R [SQL Server]

43. Reporting Systems: Producing the RFM Analysis: Stored Procedure RFM_Analysis [SQL Server]

44. Reporting Systems: Producing the RFM Analysis: RFM Results [SQL Server] I

45. Reporting Systems: Producing the RFM Analysis: RFM Results [SQL Server] II

46. Reporting Systems: Producing the RFM Analysis: RFM Results [SQL Server] III

47. Reporting Systems: Producing the RFM Analysis: RFM Results [SQL Server] IV

48. Reporting Systems: Report Characteristics

49. Reporting Systems: Report System Functions
Report Authoring:
Connect to data sources
Create the report structure
Format the report
Report Management:
Define who receives what reports when and by what means
Report Delivery:
Push reports or allow them to be pulled

50. Reporting Systems: OnLine Analytical Processing [OLAP]
An OLAP report has measures and dimensions:
Measure—a data item of interest
Dimension—a characteristic of a measure
OLAP cube—a presentation of a measure with associated dimensions.
An OLAP cube can have any number of axes.
The terms OLAP cube and OLAP report are synonymous.
OLAP allows drill-down—a further division of the data into more detail.

51. Reporting Systems: OLAP Reports I

52. Reporting Systems: OLAP Reports II

53. Reporting Systems: OLAP Reports III

54. Reporting Systems: OLAP Reports IV

55. Reporting Systems: OLAP Reports V

56. Reporting Systems: OLAP Drill Down I

57. Reporting Systems: OLAP Drill Down II

58. Reporting Systems: OLAP Servers and OLAP Databases

59. Summary Data analytics Data mining Predictive analytics
Provides advanced data analysis tools to extract knowledge from business data
Data mining
Automates the analysis of operational data to find previously unknown data characteristics, relationships, dependencies, and trends
Predictive analytics
Uses information generated in the data-mining phase to create advanced predictive models

60. Summary (cont’d.) Online analytical processing (OLAP)
Advanced data analysis environment that supports decision making, business modeling, and operations research
SQL has been enhanced with extensions that support OLAP-type processing and data generation