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Data WarehouseEnablement Through
Bi-Level Data Modeling
Charles W. Bachman
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Overview:
• decision support premise
• decision support problem
• data warehouse solution
• data complexity problem
• “bi- level” data model solution
✔
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Decision Support Premise
That there is valuable information lockedup in the current operational systems andother sources which, if it were readilyavailable, would contribute to improvedbusiness decision making and lead to amore efficient and profitable business.
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Overview:
• decision support premise
• decision support problem
• data warehouse solution
• data complexity problem
• “bi- level” data model solution
✔
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Decision Support Problem
• many, independently developedoperational systems
• inadequate capacity and performance
• inappropriate data organization
• conflicting objectives
• insufficient data content
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Integrated Database Solution
• single, integrated database
• unchallenged capacity and performance
• multiple, concurrent data organizations
• unified objectives
• integrated, current, archival andexternal data
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Integrated Database Solution
single, integrated, all-purpose database
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Integrated Database Solution
• idealized
• impractical
• too risky
• not in our time
• maybe never
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Overview:
• decision support premise
• decision support problem
• data warehouse solution
• data complexity problem
• “bi- level” data model solution
✔
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Data Warehouse Solution
operationalsystems data warehouse DSS
users
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Data Warehouse Solution
• pragmatic
• practical
• minimize risk
• compromising
• potentially, very expensive
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Data Warehouse Solution (stage 0)
operationalsystems data warehouse DSS
users
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Data Warehouse Solution (stage 1)
data warehouse
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Data Warehouse Solution (stage 1)
data warehouse
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Data Warehouse Solution (stage 2)
data warehouse
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Data Warehouse Solution (stage 3)
data warehouse
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Data Warehouse Solution (stage 4)
datamart
datawarehouse
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Data Warehouse Solution (stage 4)
The “data warehouse” in the stage 4 solutionprovides the one, consistent, integrated database thatwas sought by the “integrated database” solution.
All data marts are constructed from the informationobtained from that single data warehouse and thusyield consistent answers to all users.
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OperationalSystems
repetitive decisions
limited data
response intensive
frequent update
operational worker
Decision SupportSystems
one of a kind decisions
data intensive
relaxed response
read only
knowledge worker
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OLTP versus OLAP
The term “Online TransactionProcessing” (OLTP) is used tocharacterize operational systems.
The term “Online Analytic Processing”(OLAP) is used to characterize the newerdecision support systems.
But look out for “data mining.”
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OLAP/ROLAP/MOLAP/DOLAP
• OLAP
• Relational - OLAP
• Multidimensional - OLAP
• Distributed - OLAP
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Data Mining(off-line analytical processing)
Data mining is executed by batchprograms, (demons) which, acting ontheir own, intelligently survey massesof data looking for significant patternsthat are obscured by the sheer mass ofthe available data.
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Overview:
• decision support premise
• decision support problem
• data warehouse solution
• data complexity problem
• “bi- level” data model solution
✔
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All Kinds of Data
• base and summary data
• current and archival data
• internal and external data
• content and context data
• real and meta data
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Base and Summary Data
• last sales transaction
• individual sales transactions for the last 24 hours
• summary of sales by store, product and hourof day, for the last 24 hours
• summary of daily sales, for the last thirteen weeks
• summary of monthly sales, by district, for thelast ten years
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Current and Archival Data
• current sales, current inventory, currentbank balances, current salaries, etc.
• sales transactions for the past ten years• personnel records as they have appeared,
change by change, since employment• claims history by insurance policy,
since the policy was written
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Internal and External Data
Internal Data• company’s truck sales• company’s automobile sales
External Data• industry truck sales• industry automobile sales
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Content and Contextual Data
• changed sales district boundaries• changed product grouping• changed accounting rules• changed pricing structure• changed product packaging• changed fiscal yearQuestion:How are the numbers to be understood?
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Seventy Five (75) Units
• kilograms?• gallons?• feet?• cubic feet?• cartons?• metric tons?• cars?
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Why were the Chicago districtsales so low in 1996?
92 93 94 95 96
trend line
?$
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Real and Meta Data “meta” order no date quantity
12498 May 1 10 12944 May 7 21
“real” 13001 June 6 75
13749 July 9 15
14992 Aug 9 23
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Data and Data ModelA data model describes the data in a database.
database data
data model
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Data Models,with Their DataDescriptions,Control Access toDatabases.
database
datamodel
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OperationalDatabases,with TheirData Models
datamodel
datamodel
datamodel
datamodel
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Data Transformationsand Data Model Maps
operationalsystems
DSSusers
datawarehouse
datamodel
datamodel
datamodel
database database
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Single Level Data Modelsand Their Data Model Maps
operationalsystems
DSSusers
datawarehouse
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m x (m - 1) Mappings
If there are “m” data models, then thereare potentially m x (m - 1) mappings tocreate and maintain.
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Overview:
• decision support premise
• decision support problem
• data warehouse solution
• data complexity problem
• “bi- level” data model solution✔
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Bi-Level Data Models
operationalsystems
DSSusers
datawarehouse
logical
logicaldata model
physical
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“m” Mappings
With Bi-Level Data Modeling, if thereare “m” physical data models, there areonly the m mappings, one between eachphysical data model and the singlelogical data model.
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DBA’s Challenge
The complexity of the data warehousesolutions, with so many operationalsystems, external sources, data martsand a single integrating data warehouse,creates a real challenge for the databaseadministrators.
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Meta Data Focus
Just keeping track of the various datadescriptions used by the various files,databases, programs, and the mappingsthat join them, is a challenge.
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Bi-level data models must handle a number ofprogramming languages, query languages, anddatabase management systems:
• COBOL• PL/1• IMS• IDMS• many varieties of SQL• Object Oriented databases• Multidimensional databases• and whatever might come next
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Data Warehouse (stage 4)
datamart
datawarehouse
DSSusers
operationalsystems
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Data Warehouse Solution (stage 1)
datamart
DSSusers
operationalsystems
format 1
format 2
format 3
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The “bi-level data model” approachoperates on the well known principleof “abstraction.”
It uses a single, higher level logical datamodel (conceptual schema) to understandthe semantics of differing physical leveldata models.
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Levels of Abstraction
Logical Data Model
Physical Data Model
Machine Model
Micro Code Model
Physical Model
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Meta Data Challenge
While the single, integrated databasesolution to operational and decisionsupport systems seems continually tobe beyond reach, the single, integratedmeta data model to support the datawarehouse solution is available today.
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Logical Data Model
• key to bi-level data modeling• provides platform independence• target for reverse engineering existing databases and files• source for forward engineering to new databases• synchronizes logical and physical descriptions
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Physical Modelsfor Data Warehousing
• spread sheets
• multidimensional schema
• star schema
• snowflake schema
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Spread Sheet
1 - fact2 - dimensions
CELL
SHEET
ROW COLUMN
a
1
2
3
4
b c d
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Spread Sheet
1 - fact2 - dimensions
SHEET
CUSTOMER
ORDER
PRODUCT
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Cube
1 - fact3 - dimensions
CUSTOMERSHIPMENT
STOCK ITEM
PRODUCT
3-DCUBE
CUSTOMERSHIPMENTCUSTOMER CUSTOMER
SHIPMENT
STOCK ITEMORDER
LOCATION
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Multidimensional Cube
1 - fact4 - dimensions, or more
CUSTOMERSHIPMENT
RESTOCKITEM
STOCK ITEM
PRODUCT LOCATION
MULTI-DCUBE
CUSTOMERSHIPMENTCUSTOMER CUSTOMER
SHIPMENT
STOCK ITEMORDER
TIMEPERIOD
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Summary Data in the Cube
1 - set of summarized facts4 - dimensions YEARLY
TOTAL
MONTHLYTOTAL
CUSTOMERSHIPMENT
RESTOCKITEM
STOCK ITEM
PRODUCT LOCATION
MULTI-DCUBE
CUSTOMERSHIPMENTCUSTOMER CUSTOMER
SHIPMENTTIME
PERIOD
DAILYTOTAL
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Star Schema
1 - fact4 - dimensions
product
location
time period
order
customer
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Snow Flake Schema
product
location
district
store type
time period
order
customer
salesman
age group
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Snow Flake Schema
product
location
district
store type
time period
order
customer
salesman
age group
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Snow Flake Schema
product
location
district
store type
day of week
time of day
time period
order
customer
salesman
age group
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Snow Flake Schema
TIMEOF DAY
CUSTOMERSHIPMENT
RESTOCKITEM
STOCK ITEM
PACKAGE DISTRICT
MULTI-DCUBES
CUSTOMERSHIPMENTSOURCECUSTOMER
SHIPMENTCUSTOMERSHIPMENT
CUSTOMERSHIPMENT
STOCK ITEMSUMMARY
STORETYPE
CUSTOMERSHIPMENT
DAYOF WEEK
CUSTOMERSHIPMENT
RESTOCKITEM
PRODUCT LOCATIONCUSTOMERSHIPMENTCUSTOMER CUSTOMER
SHIPMENTTIME
PERIOD
SALESPERSON
AGEGROUP
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Multiple Multi-Cube Databases
orders purchases
production collections
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The OLAP/MOLAP/ROLAP/DOLAP view ofthe business world is a narrow view, designedand organized to answer questions about aspecified type of fact, quickly and easily. Itoffers a rifle shot into the designatedcollection of corporate data.
Multi-disciplinary questions crossing fromone star schema or multidimensional cube toanother generally require separate queries bythe person raising the question.
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Summary: Bi-LevelData Model Architecture
• records the flow of data,• defines the necessary data transformations,• assures data consistency, and• catalogues the available data for decision support workers.