converting an essential entity/relationship model into a real database design

Copyright © 2009 David C. Hay

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Converting An Essential Entity/Relationship Model Into A Real Database Design

Enterprise Data WorldDavid Hay

Tampa, FloridaApril 6, 2009

David C. Hay

Enterprise Data WorldAustin, TexasMay 1, 2014

Essential Strategies International13 Hilshire Grove LaneHouston, TX 77055http://essentialstrategies.com

Copyright © 2014 David C. Hay 2

Different points of view . . .

Data modeler

End User

Designer


Data Modeler’s Assignment . . .

Capture the language of the business Do so in as flexible and robust a manner as

possible.

Data modeler


How to achieve flexibility and robustness . . . Generalize entity classes,

Each describes as large a population of phenomena as possible.

For example, a Party is a Person or an Organization that is of interest to the company. Organization in this case can then have more specific sub-types, like Company, Government Agency, Household, etc.

Separate roles from the definitions of things. For example, an “Employee” is a Person who is

employed by an Organization, such as a company. A “Vendor” is a Party who is a vendor in an Order.


How to achieve flexibility and robustness . . .

Put as much of the language of the business as instances of ...type entity classes. This includes categories, like Activity Type and

Product Type.

Treat nearly all attributes as being multi-valued, requiring a separate entity class.For example, Party Characteristic, with an intersect

entity class Party Characteristic Value each instance of which contains a “Value” of a Party Characteristic for a particular Party.

Essential Data Model – General conceptsSuper-set of user views


The End User’s view . . .

The end user, on the other hand deals with very concrete, particular things.

The user interface must reflect the way the user deals with things today.

The behavior of the system is an extension of the user’s behavior.

Ideally ‘e participated in the modeling and agreed with the overall concepts.

But those abstractions have little to do with today’s problems.

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End User


Designer’s Assignment . . .

Designers may not be experienced with models this abstract.

This paper is intended to present some of the more basic steps required to convert an essential data model into a database design.

It turns out that abstract models are implemented using the same steps as not so abstract models.


Connections to System Users . . .

Conceptual Data Model – General conceptsSuper-set of user views

User Views – Concrete termsHabits and personal preferences

Database and Application DesignTrue to conceptual modelAccommodates technological limitsMakes user views possible


UML Alert!

Both the Essential Data Model and the Relational Database Design shown here use constrained versions of the Unified Modeling Language.

Translation:

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UML Symbol Essential Symbol

Design Symbol

Class Entity Type Table

Association Relationship Foreign Key

Attribute Attribute Column

Inheritance Sub-type (resolved)


Four Steps to Design

1. Resolve sub-types.2. Perform default database design3. Design computed columns.4. De-normalize as necessary.5. Deal with those parameters.



1. Perform default database design

2. Resolve sub-types.

3. Design computed columns.

4. De-normalize as necessary.

5. Deal with those parameters


The Default Database Design . . .

Each entity class becomes a table.

Each attribute becomes a column.

Each primary identifier becomes a primary key. Each component of the identifier is a reference to a column in the table.

Each role on the “many” end of a relationship becomes a foreign key, composed of pointers to the columns in the other table’s primary key.

If a relationship from table A to table B was part of a unique identifier, the columns in table A that are the foreign key implementation of that relationship become part of the primary key for Table A.


{id}

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An Entity/Relationship Diagram . . .

Identifiers


The Default Conversion . . .

Primary Keys

Foreign Key Columns

Foreign Keys


Resolve sub-types

V1: One table for the super-type.All attributes from all sub-types become columns.Cannot meaningfully enforce mandatory columns.Requires adding “type” column.

V2: One table for each sub-type.Attributes for super-type plus sub-type form

columns for each sub-type table.Foreign keys for each relationship linked to super-

type in each sub-type table.

V3: CombinationsMost complex.Most “true”.


For example, this model . . .

Implement Sub-types

Inherit super-type attributes

Collapse un-implemented sub-types


Could be implemented, thus . . .

“Department number is only required if “Organization Type Name” is “Department”.


Criteria

Relative frequency of sub-type retrieval? Who is going to do it?

Different populations?Different timings?

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Design Computed Columns . . .

Compute on input: if values are relatively stable, or if retrieval volume per day is significantly greater than update

volume. Maintenance is required to keep values consistent.

Compute on output: if values are relatively dynamic, or if retrieval volume is relatively low. Additional maintenance is unnecessary.

Kinds of calculations: Simple: A*B+C Inference: INFER-THRU (<relationship>,<parent entity>,

<target attribute>) Summation: SUM-THRU (<relationship>, <child entity>,

<target attribute>)


For example, this model . . .

/PriceINFER-THRU (to buy, Product Type, Unit Price)/Value =Quantity * Price

/Contract Value =SUM-THRU (composed of, Line Item, Value)

/Total Sales to Date =SUM-THRU (bought via, Line Item, Value)


Could be implemented thus . . .

Computed on input (and stored)

Computed on query*

* Note that translating the formula into, for example, a stored procedure, is left to the viewer.


De-normalize (three methods) . . .

1. Inherit reference values.

2. Split tables horizontally (by instance).

3. Split tables vertically (by column).


PARTY# GLOBAL IDENTIFIER* NAME

PARTY TYPE# NAME* DESCRIPTION

LINE ITEM# LINE NUMBER* QUANTITYo COST* (VALUE)

PHYSICAL ASSET SPECIFICATION# GLOBAL IDENTIFIER* DEFAULT NAME* DESCRIPTION* EFFECTIVE DATEo DISCONTINUE DATEo STANDARD PRICE* (TOTAL SALES VALUE)

CONTRACT# CONTRACT NUMBER* ISSUE DATE* (TOTAL VALUE)

PERSON

ORGANIZATION* DESCRIPTION

PRODUCT MATERIAL

GOVERNMENT

GOVERNMENTAGENCY

OTHERORGANIZATION

COMPANY

INTERNALORGANIZATION

an example of

embodied in

for

purchased via

a sub-type of

a super-type of

from

buyer in

composed of

part of

seller in

to

Inherit from reference tables . . .

buyer in

seller in

This model . ..


Could be implemented as . . .

CONTRACTSCONTRACT_NUMBER(PK)ISSUE_DATETOTAL_VALUEBUYER_NAMESELLER_NAME

LINE_ITEMCONTRACT_NUMBER(FK)PRODUCT_NAMEQUANTITYCOSTSTANDARD_PRICEVALUE


Note:

When replicating values, recognize the maintenance required to keep them consistent.

Note that the paradigm of INFER-THRU and SUM-THRU already anticipated this.

If these are implemented as dynamic columns, maintenance is automatic.

If they are implemented as static copies, maintenance must be added.

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Have you heard this before?

Denormalization replicates

computed fields


Split Horizontally (instances) . . .

For example, By Geographic Area

Some tables for North American customers Some tables for European customers Etc.

By Customer Type, etc. Some tables for corporate customers Some tables for individual customers Etc.

Note the problems that will arise if a significant number of customers (for example) fall into more than one category.


Split Vertically (columns) . . .

For example, People with customer attributes

Annual sales Sales representative Etc.

People with employee attributes Social security number Employment date

Note that people with both kinds of attributes would appear redundantly in both tables.


NOTE . . .

De-normalization optimizes some operations at the expense of others.

Test the effects before making them permanent.

Document the rationale for the de-normalization.







5. Deal with those parameters.


About those parameters . . .

Some entity classes (Party, for example) invariably have a lot of attributes.

And they change over time.

Their definitions change over time.

We need an alternative.

Define attributes as data.

Also called:

Characteristics,

Parameters,

Variables,

Etc.

Here’s an approach for Party, for example


Party parameters as “Characteristics” . . .

PARTY CHARACTERISTIC:

“Height”“Number of employees”“Regulatory target”,Etc.

PARTY CHARACTERISTIC VALUE:

“Height” of “Jerry Smith” has CHARACTERISTIC VALUE of “6.1” (feet)…

according to “Jerry Smith”.


Party Characteristic Constraints . . .NOTE: the CONTINUOUS PARTY CHARACTERISTIC “Height”

-- may only be used as a PARTY CHARACTERISTIC VALUE

-- for a PARTY that is an example of the PARTY TYPE “Person”.


Designing those parameters . . .

While it is a powerful way of dealing with the complexity of data …

…the Parameter Model makes common manipulations harder, however.

Convert parameters that are . . . Relatively stable Not multi-valued (Over time?)

Do not convert parameters that are . . . Multi-valued Changeable over time and this must be reported.


For example, in this model . . .


These Characteristics:

Could be implemented as:

Can be implemented thus . . .

PEOPLEBIRTHDATE HEIGHT

PARTY CHARACTERISTIC

Name Description (Party Type)Birthdate The day the person appeared PersonHeight Vertical distance PersonAnnual Sales Average sales in a year CompanyTax ID IRS tax identifier Company

COMPANIESANNUAL SALES TAX IDENTIFICATION NUMBER

Bad idea!







5. About those parameters.

6. About those user views.


In summary: About Those User Views . . .

The database designer need only balance data model integrity with performance issues.

The application designer must take the data as organized in a database and present it reasonably to each particular end user.

This requires skill in understanding both the database and the underlying data model.


Questions . . . ?

converting an essential entity/relationship model into a real database design

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