ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis)

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ACCTG 6910Building Enterprise &

Business Intelligence Systems(e.bis)

ACCTG 6910Building Enterprise &

Business Intelligence Systems(e.bis)

Dimensional Modeling II

Olivia R. Liu Sheng, Ph.D.Emma Eccles Jones Presidential Chair of Business

Olivia R. Liu Sheng, Ph.D.Emma Eccles Jones Presidential Chair of Business

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TechnicalArchitecture

Design

TechnicalArchitecture

Design

ProductSelection &Installation

ProductSelection &Installation

End-UserApplication

Specification

End-UserApplication

Specification

End-UserApplication

Development

End-UserApplication

Development

The Business Dimensional Lifecycle

ProjectPlanningProject

Planning

Business

Requirement

Definition

Business

Requirement

Definition

DeploymentDeploymentMaintenance

andGrowth

Maintenanceand

Growth

Project ManagementProject Management

DimensionalModeling

DimensionalModeling

PhysicalDesign

PhysicalDesign

Data StagingDesign &

Development

Data StagingDesign &

Development

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Outline

• Table structure, types, characteristics and terminology

• Design steps• Dimensional models with varying

types of fact and dimension tables

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Types of Facts

• Transactional facts (transactions or line items in transactions)

• Snapshots• Factless facts

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Types of Dimensions

• Role playing dimensions• Heterogeneous dimensions• Slowly changing dimensions• Large dimensions• Many-to-many dimensions

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Keys and Attributes

• Primary key - a column whose value uniquely identifies each row (record) in the table.

• Attributes – columns in a table that are not designated as the primary key.

• Foreign key – a non-primary-key attribute for a table that corresponds to a primary key of another table.

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Attributes in DW tables

• Dimension Table– One Primary Key– Dimension Attributes

• Fact table– Primary key --- A collection of primary keys from

all its associated dimension tables• All warehouse keys in fact table are foreign keys

referring to its associated dimension tables• All/part of warehouse keys in fact table form the

primary key of fact table

– Fact Attributes

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Attributes in DW tables

SALES# TIME_KEY# PRODUCT_KEY# CUSTOMER_KEY* PRICE* QUANTITY* SALES

CUSTOMER# CUSTOMER_KEY* CID* CNAME* STATE* CITY

PRODUCT# PRODUCT_KEY* PID* PNAME* PCNAME

TIME# TIME_KEY* ORDERDATE* DAY_OF_WEEK* DAY_NUMBER_IN_MONTH* DAY_NUMBER_IN_YEAR* WEEK_NUMBER* MONTH* QUARTER* HOLIDAY_FLAG* FISCAL_YEAR* FISCAL_QUARTER

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Data warehouse keys generated by the system

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Keys and Grain

• Keys– Primary or natural keys (from source

systems)– Warehouse or synthetic keys

(generated by a data warehouse tool)• Grain

– The level of detail of fact measures described in the DW, e.g., sales transactions from order line items by order date, product and customer

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Single-Fact-Table Data Warehouse Design Decisions

1. The business questions in focus and source information systems*

2. The grain of the fact table3. The dimensions tables and keys4. The fact attributes and dimension

attributes

*All DW attributes must be mapped to or derived from source attributes

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1. The business questions in focus and source information systems


attributes

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Sample Business Questions

• Report Sales in terms of – (total) amt, (total) qty and (avg.) price

• Report Sales by PRODUCT name and/or category name

• Report Sales by CUSTOMER name, city and/or or state

• Report Sales by ORDER date, month, year, holiday, special event or other time constraints

• Report using a combination of the measures and constraints

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Relational Schema of B.com B2B System

Orders ( Order_No, SID, BID, CID, Order_date)

OrderLine (Order_No, Line_ID, PID, Actual_Del_Date, Target_Del_Date, Arrival_Date, Shipping_Fee, Tax, Quantity, Unit_Price,Defect_on_arrival)

Delivery ( SID, CID, Unit_shipping_fee, UNIT_DEL_TIME)

Contract ( CID, Contract_Name, Payment_term, Payment_num)

Payment ( PaymentID, OrderNO, Pay_Amount, Date)

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Relational Schema of B.com B2B SystemCategory ( CAT_ID, CAT_Name)

Product ( PID, CAT_ID, P_Weight, P_Life, P_Name)

Supplier ( SID, S_Name, S_City, S_State, S_Country)

Product_Supply ( PID, SID, Unit_Price, Quantity_in_Stock, Production_in_Week)

Buyer ( BID, B_Name, CityID, B_Type)

Buyer_City ( CityID, C_Name, C_State, C_Country, C_Tax)

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attributes

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Grain of the Fact Table

Type of fact table: transactional facts

Potential grains: order or orderlineConstraints: order date, product,

customerGrain: sales from orderline (by

order date, product, and customer)

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attributes

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Dimension Tables and KeysKey dimension tables jointly make up the primary key for a fact table

TIME# TIME_KEY* ORDER_DATE* DAY_OF_WEEK* DAY_NUMBER_IN_MONTH* DAY_NUMBER_IN_YEAR* WEEK_NUMBER* MONTH* QUARTER* HOLIDAY_FLAG...

SALES# TIME_KEY# CUSTOMER_KEY# PRODUCT_KEY* PRICE* QUANTITY* SALES_AMOUNT


CUSTOMER# CUSTOMER_KEY* CID* CNAME* CITY* STATE

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attributes

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Determine Fact Attributes

SALES# TIME_KEY# CUSTOMER_KEY# PRODUCT_KEY* PRICE* QUANTITY* SALES_AMOUNT

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Types of Fact Attributes

• Additive fact attributes can be added along any dimension.





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• Non-additive fact attributes cannot be added along any dimension.





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• Semi-additive fact attributes can be added along some dimensions.

INVENTORY_PRODUCT# PRODUCT_KEY

WAREHOUSE# WAREHOUSE_KEY

INVENTORY_TIME# TIME_KEY

INVENTORYFACT# TIME_KEY# PRODUCT_KEY# WAREHOUSE_KEY* QUANTITY_ON_HAND

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Time Dimension

• Data warehouse needs an explicit time dimension table instead of just a time attribute (e.g, ORDERDATE).

• Save computation effort and improve query performance

• Complex queries regarding calendar calculation are hidden from end users of data warehouse.

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Time Dimension

Besides the time attribute, time dimension table includes the following additional attributes:

– Day_of_week (1-7); Day_number_in_month (1-31); – Day_number_in_year (1-365)– Week_number (1-52); month (1-12), Quarter (1-4)– Holiday_flag (y/n)– Fiscal_quarter, Fiscal_year

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Determine Dimension Attributes

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Avoid Snowflake Designs

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Avoid Snowflake Design

PRODUCT_CATEGORY# PRODUCT_CATEGORY_KEY* PCID* PCNAME

CUSTOMERTIME

SALES

PRODUCT# PRODUCT_KEY* PID* PNAME* PRODUCT_CATEGORY_KEY

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Snowflake structure

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Avoid Snowflake Schemas

• Tradeoff of avoiding snowflake

– Advantage: improve query performance and easy of understanding

– Disadvantage: require more storage space

ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis)

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