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Data WarehousingData Warehousing
Lecture-14Lecture-14
Process of Dimensional ModelingProcess of Dimensional Modeling
Virtual University of PakistanVirtual University of Pakistan
Ahsan Abdullah
Assoc. Prof. & Head
Center for Agro-Informatics Research
www.nu.edu.pk/cairindex.asp
National University of Computers & Emerging Sciences, Islamabad
Email: ahsan@cluxing.com

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Process of Dimensional ModelingProcess of Dimensional Modeling

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The Process of Dimensional ModelingThe Process of Dimensional Modeling
Four Step Method from ER to DMFour Step Method from ER to DM
1.1. Choose the Business ProcessChoose the Business Process
2.2. Choose the GrainChoose the Grain
3.3. Choose the FactsChoose the Facts
4.4. Choose the DimensionsChoose the Dimensions

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Step-1: Choose the Business ProcessStep-1: Choose the Business Process
 A business process is a major operationalA business process is a major operational
process in an organization.process in an organization.
 Typically supported by a legacy systemTypically supported by a legacy system
(database) or an OLTP.(database) or an OLTP.
 Examples: Orders, Invoices, Inventory etc.Examples: Orders, Invoices, Inventory etc.
 Business Processes are often termed asBusiness Processes are often termed as
Data Marts and that is why many peopleData Marts and that is why many people
criticize DM as being data mart oriented.criticize DM as being data mart oriented.

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Star-1
Star-2
Snow-flake
Step-1: Separating the ProcessStep-1: Separating the Process

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Step-2: Choosing the GrainStep-2: Choosing the Grain
 Grain is the fundamental, atomic level of data to beGrain is the fundamental, atomic level of data to be
represented.represented.
 Grain is also termed as the unit of analyses.Grain is also termed as the unit of analyses.
 Example grain statementsExample grain statements
 Typical grainsTypical grains
 Individual TransactionsIndividual Transactions
 Daily aggregates (snapshots)Daily aggregates (snapshots)
 Monthly aggregatesMonthly aggregates
 Relationship between grain and expressiveness.Relationship between grain and expressiveness.
 Grain vs. hardware trade-off.Grain vs. hardware trade-off.

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Step-2: Relationship b/w GrainStep-2: Relationship b/w Grain
Daily aggregates
6 x 4 = 24 values
Four aggregates per week
4 x 4 = 16 values
Two aggregates per week
2 x 4 = 8 values
LOW Granularity HIGH Granularity

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The caseThe case FORFOR data aggregationdata aggregation
 Works well for repetitive queries.Works well for repetitive queries.
 Follows the known thought process.Follows the known thought process.
 Justifiable if used for max number of queries.Justifiable if used for max number of queries.
 Provides a “big picture” or macroscopic view.Provides a “big picture” or macroscopic view.
 Application dependent, usually inflexible toApplication dependent, usually inflexible to
business changes (remember lack ofbusiness changes (remember lack of
absoluteness of conventions).absoluteness of conventions).

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The caseThe case AGAINSTAGAINST data aggregationdata aggregation
Aggregation is irreversible.Aggregation is irreversible.
 Can create monthly sales data from weekly salesCan create monthly sales data from weekly sales
data, but the reverse is not possible.data, but the reverse is not possible.
Aggregation limits the questions that can beAggregation limits the questions that can be
answered.answered.
 What,What, whenwhen, why,, why, wherewhere, what-else, what-next, what-else, what-next

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The caseThe case AGAINSTAGAINST data aggregationdata aggregation
Aggregation can hide crucial facts.Aggregation can hide crucial facts.
The average of 100 & 100 is same as 150 & 50The average of 100 & 100 is same as 150 & 50

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Aggregation hides crucial factsAggregation hides crucial facts
ExampleExample
Week-1 Week-2 Week-3 Week-4 Average
Zone-1 100 100 100 100 100
Zone-2 50 100 150 100 100
Zone-3 50 100 100 150 100
Zone-4 200 100 50 50 100
Average 100 100 100 100
Just looking at the averages i.e. aggregate

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Aggregation hides crucial factsAggregation hides crucial facts
chartchart
0
50
100
150
200
250
Week-1 Week-2 Week-3 Week-4
Z1 Z2 Z3 Z4
Z1: Sale is constant (need to work on it)
Z2: Sale went up, then fell (need of concern)
Z3: Sale is on the rise, why?
Z4: Sale dropped sharply, need to look deeply.
W2: Static sale

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“We need monthly sales
volume and Rs. by
week, product and Zone”
Facts
Dimensions
Step 3: Choose Facts statementStep 3: Choose Facts statement

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 Choose theChoose the factsfacts that will populatethat will populate
each fact table record.each fact table record.
 Remember that best Facts are Numeric,Remember that best Facts are Numeric,
Continuously Valued and Additive.Continuously Valued and Additive.
 Example: Quantity Sold, Amount etc.Example: Quantity Sold, Amount etc.
Step 3: Choose FactsStep 3: Choose Facts

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 Choose theChoose the dimensionsdimensions that apply tothat apply to
each fact in the fact table.each fact in the fact table.
 Typical dimensions: time, product,Typical dimensions: time, product,
geography etc.geography etc.
 Identify the descriptive attributes thatIdentify the descriptive attributes that
explain each dimension.explain each dimension.
 Determine hierarchies within eachDetermine hierarchies within each
dimension.dimension.
Step 4: Choose DimensionsStep 4: Choose Dimensions

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Step-4: How to Identify a Dimension?Step-4: How to Identify a Dimension?
 The single valued attributesThe single valued attributes during recording of aduring recording of a
transactiontransaction are dimensions.are dimensions.
Calendar_Date
Time_of_Day
Account _No
ATM_Location
Transaction_Type
Transaction_RsTransaction_Rs
Fact Table
Dim
Time_of_day:Time_of_day: Morning, Mid Morning, Lunch Break etc.
Transaction_Type:Transaction_Type: Withdrawal, Deposit, Check balance etc.

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Step-4: Can Dimensions be Multi-valued?Step-4: Can Dimensions be Multi-valued?
 Are dimensions ALWYS single?Are dimensions ALWYS single?
 Not reallyNot really
 What are the problems? And how to handle themWhat are the problems? And how to handle them
 Calendar_Date (of inspection)
 Reg_No
 Technician
 Workshop
 Maintenance_Operation
 How many maintenance operations are possible?How many maintenance operations are possible?
 FewFew
 Maybe more for old cars.Maybe more for old cars.

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Step-4: Dimensions & GrainStep-4: Dimensions & Grain
 Several grains are possible as per businessSeveral grains are possible as per business
requirement.requirement.
 For some aggregations certain descriptions do notFor some aggregations certain descriptions do not
remain atomic.remain atomic.
 Example: Time_of_Day may change several timesExample: Time_of_Day may change several times
during daily aggregate, but not during a transactionduring daily aggregate, but not during a transaction
 Choose the dimensions that are applicableChoose the dimensions that are applicable
within the selected grain.within the selected grain.