1. Zhangxi Lin Texas Tech University
ISQS 6339, Data Management and Business Intelligence Cubism – Measures and Dimensions
Zhangxi Lin
Texas Tech University

2. Outline Measures Where we’ve been Populating fact table
Types of dimensions

3. Structure and Components of Business Intelligence
SSMS
SSIS
SSAS
SSRS
SAS EG
SAS EM

4. Snowflake Schema of the Data Mart
Manufacturingfact
DimBatch 10 9
DimMachine 8
DimProduct 3
DimMachineType
DimPlant 7 5
DimProductSubType 2
DimMaterial
DimCountry 6 4
DimProductType 1

5. Where we’ve been and where we are now
Exercise 1: Getting started
Exercise 2: Creating a data mart with SSMS
Exercise 3: Creating data mart with BIDS
Exercise 4: Populating dimensions of a data mart
Exercise 5: Loading fact tables
Exercise 6: Create and customize a cube

6. What we need to do with the half-done data mart?
Populate DimBatch dimenstion table
Populate ManufacturingFact table
Build an OLAP cube (we already did this before)
Check measures
Check dimensions

7. MEASURES

8. Facts
Facts are measurements associated with a specific business process.
Many facts can be derived from other facts, including additive and semiadditive facts.
Non-additive facts can be avoided by calculating it from additive facts.
Measures are clustered together in a group, called measure group.

9. Types of measures Three types Aggregate functions
Additive measures. Most facts are additive (calculative), such as sum
Semiadditive measures. The measures that can be added along some dimensions, but not along others. For example, inventory level can be added along product dimension but not time dimension.
Non-additive (such as max, average), or descriptive (e.g. factless fact table).
Aggregate functions
Additive: Sum
Semiadditive: ByAccount, Count, FirstChild, FirstNonEmpty, LastChild, LastNonEmpty, Max, Min
Nonadditive: DistinctCount, None.

10. Measures and dimensions
Dimensions are used to aggregate measures. Therefore, they must be somehow related to measures
Granularity
Important for the analysis
There could be missing values in the fact table

11. LOADING FACT TABLES

12. Exercise 5: Loading Fact Tables
Project name: MMMFactLoad-lastname
Package name: FactLoad.dtsx
Tasks
Create Inventory Fact table
Load Dim Batch
Load Manufacturing Fact
Load Inventory Fact
Deliverable: Upload the screenshot to the Blackboard system.

13. Inventory Fact Table Create a Table InventoryFact in your database.
Compound primary key: DateOfInventory, ProductCode, and Material
Define two foreign keys
Column Name
Data Type
Allow Nulls
InventoryLevel
Int No
NumberOnBackorder
DateOfInventory
Datatime
ProductCode
Material
Varchar(30)

14. Data Sources for Loading Fact
For loading DimBatch table and ManufacturingFact table
BatchInfo.CSV
For loading InventortyFact table
Lin.OrderProcessingSystem Database

15. Control Flow for Loading Facts and the Remaining Dimension
Note: to ease debugging, you may use three packages and test them one by one, instead of doing everything in one package

16. Flat File Connection
Data types
BatchNumber, MachinNumber: four-byte signed integer [DT_I4]
ProductCode, NumberProduced, NumberRejected: four-byte signed integer [DT_I4]
TimeStarted, TimeStopped: database timestamp [DT_DBTimeStamp]
Only check BatchNumber as the input of Dim Batch
All columns are needed for fact tables

17. Some Frequently Used Nodes

18. Load DimBatch Data Flow

19. Debugging results

20. The Flat File Source

21. Sort Transformation
In the Aggregate item,
Define “Group-by”
BatchNumber.
In Derived column item,
Define BatchName
From BatchNumber
Use the expression
(DT_WSTR, 50)[BatchNumber]
To change the data type
Of BatchName.

22. Load Fact Data Flow

23. Derived Columns for the Fact table

24. Expressions for the Derived Columns
AcceptedProducts
[NumberProduced] – [NumberRejected]
ElapsedTimeForManufacture
DATEDIFF(“mi”, [TimeStarted],[TimeStopped])
DateOfManufacture
(DT_DBTIMESTAMP)SUBSTRING((DT_WSTR,25)[TimeS tarted],1,10)
This expression converts TimeStarted into a string and selects the first ten characters of that string. This string is then converted back into a date time, without the time portion.

25. OLE DB Destination
For loading the fact
table

26. Load Inventory Fact OLE DB Source OLE DB Destination No transformation
OrderProcessingSystem.InventoryFact
OLE DB Destination
MaxMinManufacturingDM-lastname.InventoryFact
No transformation
There are two ways to loading the table
Create the table and use ETL to load it
Import directly from the source to the database MaxMinManufacturingDM-lastname

27. Debugging Results
Loading DimBatch
Loading ManufacturingFact

28. BUILDING AN OLAP CUBE

29. Exercise 6: Design a Cube
Project name: ISQS6339_EX6_2017_lastname
Tasks
Add in new date items (year, quarter, and month) to two fact tables
Create time dimension using Manufacturing Fact table
Define calculated measures (Total Products, Percent Rejected)
Define hierarchies of attributes in dimension tables
Create a cube from the MaxMinManufacturing data mart with hierarchical date dimension
Deliverable:
Screenshots: dimension hierarchies, dimensions, relationships of facts and dimensions, deployment result, format of measures, and browsing results. 29

30. Three Steps to Create a Cube from Data Sources
Defining data source
Defining data source view
Add in three new columns of year, quarter, and month for the two fact tables
Building a cube.
Define a new dimension Dim Time from Manufacturing Fact table
Customize the cube:
Link two fact tables in a cube
Define new primary key for Dim Time
Define calculated measures
Relate dimensions to measures

31. T-SQL Expressions for DS View Definition - Manufacture
YearOfManufacture
CONVERT(char(4),YEAR(DateOfManufacture))
QuarterOfManufacture
CONVERT(char(4), YEAR(DateOfManufacture)) +
CASE
WHEN MONTH (DateOfManufacture) BETWEEN 1 AND 3 THEN 'Q1'
WHEN MONTH (DateOfManufacture) BETWEEN 4 AND 6 THEN 'Q2'
WHEN MONTH (DateOfManufacture) BETWEEN 7 AND 9 THEN 'Q3'
ELSE 'Q4'
END
MonthOfManufacture
CONVERT(char(4), YEAR(DateOfManufacture)) + RIGHT('0'+CONVERT(varchar(2), MONTH(DateOfManufacture)),2)

32. T-SQL Expressions for DS View Definition - Inventory
YearOfInventory
CONVERT(char(4),YEAR(DateOfInventory))
QuarterOfInventory
CONVERT(char(4), YEAR(DateOfInventory)) +
CASE
WHEN MONTH (DateOfInventory) BETWEEN 1 AND 3 THEN 'Q1'
WHEN MONTH (DateOfInventory) BETWEEN 4 AND 6 THEN 'Q2'
WHEN MONTH (DateOfInventory) BETWEEN 7 AND 9 THEN 'Q3'
ELSE 'Q4'
END
MonthOfInventory
CONVERT(char(4), YEAR(DateOfInventory)) + RIGHT('0'+CONVERT(varchar(2), MONTH(DateOfInventory)),2)

33. Data Source View
New columns

34. Select Measures Page
Uncheck Manufacture
Fact Count

35. The finished cube

36. Cube
Structure

37. Defining a format string

38. Inventory measures
“Number on Backorder” is also set with these two parameters

39. Calculated measures – made-up facts
The definition of calculated measure is stored in the OLAP cube itself.
The actual values that result from a calculated measure are not calculated, however, until a query containing that calculated measure is executed. The results of that calculation are then cached in the cube. The cached value is then delivered to any subsequent users requesting the same calculation.
The expressions of calculation are created using a language known as Multidimensional Expression Language (MDX) script. MDX is different from T-SQL. It is a special language with features designed to handle the advanced mathematics and formulas required by OLAP analysis. This is not found in T-SQL. 39

42. DIMENSIONS in SQL Server

43. Types of Dimensions
Fact dimensions: the Dimensions created from attributes in a fact table
Parent-Child dimensions: Built on a table containing a self- referential relationship, such as a parent attribute.
Role playing dimensions: related to the same measure group multiple times; each relationship represents a different role the dimension play; for example, time dimension plays three different roles: date of sale, data of shipment, and date of payment.
To create a role playing dimension, add the dimension to the Dimension Usage tab multiple times. Then create a relationship between each instance of the dimension and the measure group.
Reference dimensions: Not related directly to the measure group but to another regular dimension which in turn related to the measure group
Data mining dimensions: the information discovered by data mining
Many-to-many dimensions: e.g. multiple ship to addresses
Slowly changing dimensions 43

44. Slowly changing dimensions
Type 1 SCD – no track
Type 2 SCD – tracking the entire history, adding four attributes: SCD Original ID, SCD Start Date, SCD End Date, SCD Status
Type 3 SCD – Similar to Type 2 SCD but only track current state and the original state; two additional attribute: SCD Start Date, SCD Initial Value

45. Add a time dimension (a fact dimension)

47. Rename time dimension

48. Date Hierarchy

50. Material Hierarchy & Plant Hierarchy

51. Product Hierarchy

52. Relating Dimensions in the Cube