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

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

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

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

Snowflake Schema of the Data Mart 4 Manufacturingfact DimProduct DimProductSubType DimProductType DimBatch DimMachine DimMachineType DimMaterial DimPlant DimCountry 1 2 3 4 5 8 6 7 910

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 5

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 6

7 MEASURES

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. 8

Types of measures Three types ◦ 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.

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

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: email a screenshot of the “green” outcome of the ETL project to isqs6347@gmail.com, with a subject title “ISQS 6339 EX5 - ” isqs6347@gmail.com 12

Inventory Fact Table Create a Table InventoryFact in your database. ◦ Compound primary key: DateOfInventory, ProductCode, and Material ◦ Define two foreign keys Column NameData TypeAllow Nulls InventoryLevelIntNo NumberOnBackorderIntNo DateOfInventoryDatatimeNo ProductCodeIntNo MaterialVarchar(30)No 13

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 15

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 16

Some Frequently Used Nodes

Load DimBatch Data Flow 19 Note: Because of duplication in the source file, we may insert An Aggregate item after the Flat File Source item.

The Flat File Source 20

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.

Derived Columns for the Fact table 23

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. 24

Load Inventory Fact OLE DB Source ◦ 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 26

28 BUILDING AN OLAP CUBE

Exercise 6: Design a Cube Project name: ISQS6339_EX6_2015_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

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 30

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) 31

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) 32

Data Source View 33 New columns

Select Measures Page 34 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

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

41

42 DIMENSIONS in SQL Server

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

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

Add a time dimension (a fact dimension)

Rename time dimension

Date Hierarchy

Material Hierarchy & Plant Hierarchy

Product Hierarchy

Relating Dimensions in the Cube

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