Download Logical Relational Data Modeling Standards

Property and Casualty Insurance Working Group
Logical Relational Data Modeling
Standards
Versio n 1.0
Property and Casualty Insurance Working Group
Jun e 16, 2008
Table of Contents
Introduction..................................................................................................................................4
Purpose....................................................................................................................................4
Document Maintenance............................................................................................................4
Scope.......................................................................................................................................4
Logical Relational Data Model Definition.......................................................................................4
ER Diagramming Conventions.....................................................................................................6
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Modeling Syntax.......................................................................................................................6
Diagramming Layout Guidelines...............................................................................................7
Normal Forms...........................................................................................................................8
Writing Definitions of Logical Objects............................................................................................9
Logical Object Definition Guidelines: ........................................................................................9
Entity Definition Guidelines: ......................................................................................................9
Attribute Definition Guidelines: .................................................................................................9
Naming Logical Objects..............................................................................................................10
Logical Object Naming Guidelines..........................................................................................10
Entity Naming Guidelines........................................................................................................11
Attribute Naming Guidelines....................................................................................................11
Relationship Naming Guidelines.............................................................................................13
Relationship Standards..............................................................................................................14
Super- types and Sub- types......................................................................................................15
Entity Keys.................................................................................................................................17
Dimensional Data Modeling........................................................................................................18
Appendix ...................................................................................................................................19
Class Words...........................................................................................................................19
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Introduction
Purpose
This document provides standards and guidance for the naming and use of objects in logical
relational data models. Logical objects are created and maintained to meet business
requirements. Accurate naming clarifies the specific nature of each logical object. Consistency
allows the logical names to have persistent value in differentiating data items. Name formation
and the use of logical modeling objects are independent of any particular data modeling tool or
relational database management system (RDBMS) platform. These logical relational data
modeling guidelines are independent of specific CASE tools.
The intention of this standard is to establish an agreed- upon basis for developing logical relational
data models in order to promote greater quality and consistency across data models and enable
objective model reviews.
Document Maintenance
To suggest improvements, changes or additions to this standard, contact:
Gail Austin
or
gail.austin@gmail.com
Harsh Sharma
sharmahw@yahoo.com
Scope
These standards apply to all logical relational data models that are developed by OMG
submission teams.
Logical Relational Data Model Definition
The relational model for database management is a database model based on predicate logic and
set theory. It was first formulated and proposed in 1969 by Edgar Codd with aims that included
avoiding, without loss of completeness, the need to write computer programs to express database
queries and enforce database integrity constraints. “Relation” is a mathematical term for “table”,
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and thus “relations” roughly means “based on tables”. It does not refer to the links or “keys”
between tables, contrary to popular belief. 1
A logical relational data model defines what an organization knows about things of interest to the
business and graphically shows how they relate to each other in an entity relationship (ER)
diagram. An entity relationship diagram is an abstract conceptual representation of structured
data. It uses standard symbols to denote the things of interest to the business (entities), the
relationships between entities and the cardinality and optionality of those relationships. The
Logical Relational Data Model, in contrast to the more abstract Conceptual Relational Data Model,
contains detailed characteristics of the entities (attributes) and their definitions. It generates the
structure of a physical data model which in turn generates a database following Model Driven
Architecture principles. It is a result of detailed analysis of the business requirements.
The following illustration shows how the logical model fits into the overall data modeling process:
1
Wikipedia – relational model
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Ultimately, the logical relational data model helps to solidify and validate business requirements
and delivers stable, flexible data structures that are easily navigated and can answer
unanticipated questions.
ER Diagramming Conventions
Modeling Syntax
The recommended notation for models is Information Engineering (IE) – “Crow’s Feet” - because
it is easier for users to interpret than the Integration Definition for Information Modeling (IDEF1X)
notation. 2
2
The choice of IE notation will be revisited when the Barker notation becomes more widely available in the
modeling tools.
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Diagramming Layout Guidelines
Orient entities so that the “toes” of a relationship’s crow’s foot always point down. This puts
fundamental entities in the top area of the diagram, and positions associative and subtype entities
in the lower area of the diagram.
Recommended crow’s feet down convention
Avoid dead crows!
CONTACT PROFILE
PERSON
Person Identifier (FK)
Contact Point Identifier (FK)
Person Identifier (FK)
First Name
Home Contact Point Indicator
Work Contact Point Indicator
Middle Name
Last Name
Legal Name
Nickname
Name Suffix
CONTACT POINT
Birth Date
Birth Place Name
Gender Code
Contact Point Identifier
PERSON
Person Identifier (FK)
CONTACT POINT
Contact Point Identifier
First Name
Middle Name
CONTACT PROFILE
Last Name
Legal Name
Person Identifier (FK)
Contact Point Identifier (FK)
Nickname
Name Suffix
Birth Date
Home Contact Point Indicator
Work Contact Point Indicator
Birth Place Name
Gender Code
Keep the relationship lines as straight as possible. Avoid unnecessary bends. Too many symbols
clutter the diagram and make it confusing to the viewer.
Avoid crossing relationship lines. Crossed lines make it difficult to understand which entities are
related.
Relationship names should be placed on the diagram so that the verbs or verb phrases are read in
a clockwise direction from one entity to the related entity.
Example:
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POLICY
Policy Identifier
covers
is covered by
EXPOSURE
Policy Identifier (FK)
Insured Object Identifier (FK)
Coverage Type Identifier (FK)
Normal Forms
Normal Forms provide a way to structure data to eliminate undesirable redundancies,
inconsistencies and dependencies. Normalization is a formalized technique for creating the most
desirable logical model for the given data and business rules. Completed logical models should
be in, at least, Boyce/Codd Normal Form (BCNF)3. For a model to be in BCNF, every entity in the
model must be in BCNF. The normal forms are summarized below:
Firs t Nor ma l For m (1NF) identifies and eliminates repeating groups and establishes a
primary key.
Secon d Nor ma l For m (2NF) identifies and removes partial- key dependencies. This applies
only to tables with composite keys.
Thir d Nor ma l For m (3NF) identifies and eliminates non- key attributes that are dependent
on other non- key attributes.
Boyce/Cod d Nor ma l For m (BCNF) identifies and eliminates key attributes that are
dependent upon other key attributes in an entity with a composite key.
3
See Wikipedia Database Normalization: http://en.wikipedia.org/wiki/Database_normalization
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Writing Definitions of Logical Objects
Good Logical Object names are important because they provide a persistent record of the unique
nature of each object. Good names cannot be developed unless the object first has a good
business definition.
Logical Object Definition Guidelines:

Use industry definitions where possible and appropriate.

Describe what the entity or attribute is – not where, when or by whom it is used.

Be clear and concise.

Write as if the reader is unfamiliar with the business area.

Use business terms rather than technical terms to express the meaning and importance to
the business.

Use mixed case according to standard business English conventions.

Do not use jargon, abbreviations or acronyms.

Do not include information that should be documented elsewhere, such as process
descriptions.
Entity Definition Guidelines:

Entity definitions should be robust and communicate the essential and unique business
nature of the entity.

Do not depend on or refer to the definition of another object in the model.

Express one concept or idea – each entity should have a unique meaning.
Attribute Definition Guidelines:

Attribute definitions should communicate the essential business nature and purpose of the
attribute.

Do not depend on or refer to the definition of another object in the model, except for
derived attributes.

Include the domain of allowed values and default value where appropriate.
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Naming Logical Objects
Logical Object Naming Guidelines

Use one or more words which are formed using the 26 letters (A- Z), the 10 digits (0- 9), and no
special characters.

Separate words in the name with one space

Spell out words completely using no abbreviations.

Use the minimum set of words for the name that completely and uniquely capture the concept
expressed in the business definition

Reflect the business nature of the object in its name

Review names and corresponding definitions with business subject matter experts and get
their approval

Express a single idea or concept in the name that is clear and self- explanatory.

Write in plain English, spelling out all terms in full using business terms as defined by the
business client or as defined in a business or industry dictionary.

Do not use the possessive form; the articles “a”, “an”, or “the”; conjunctions; verbs; or
prepositions in the name.

Do not use the names of organizations, departments, computer applications, reports,
publications, forms or computer screens in the name.

Exceptions

Acronyms – An acronym is a word formed from the initial letters of a name, as WAC for
Women’s A rmy Corps, or by combining initial letters or parts of a series of words, as
r a d a r for r a dio d etecting a nd r anging. When an acronym is widely known it may be an
exception to the no abbreviation rule. A list of exceptions should be maintained as an
appendix to this standard and subject to an approval and a governance process.

Abbreviations – if the object name is too long to fit in the space allotted by the data
modeling tool and all non- essential words have been eliminated from the name,
abbreviate the class word. If the name is still too long, find text in the name that can form
acronyms. Starting with the right- most text, apply the acronym and repeat moving left in
the name until the name fits. Hyphen – use if the correct spelling of the word contains a
hyphen (e.g. off- premises)

Slash – allowed if used in a business term (e.g. Actual/Expected)
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
Camel Case – allowed if the business term has an uppercase letter beyond the first letter
– though rarely found in formal written English, it is sometimes found in product names
or company names (e.g. NetQuote, SmartBrief)
Entity Naming Guidelines

Form a meaningful, concise, descriptive business name for the entity by extracting the
important concepts from its business definition. The name should avoid confusion with
similarly named but differently defined entities in other business areas.

Use business terms as defined by a business subject matter expert or by a business
dictionary.

Make the entity name a singular noun or noun phrase with qualifying adjectives because each
instantiation of the object represented by the entity is a single thing.

Use UPPER CASE.

Consider appending “LOOKUP” to reference entity names to make them easier to distinguish
from fundamental entities.

Do not use the words “Entity” or “Table” in the entity name unless they are part of common
business terminology.

Combine the names of the parent entities to form the name of the associative entity if that
forms a meaningful business name. For example, PERSON SKILL describes the association
between the PERSON and SKILL entities. In other cases, the noun form of the relationship
verb may form the associative entity name as in POLICY describes the association between
PARTY and PARTY.
Attribute Naming Guidelines

Form a meaningful, concise, descriptive business name for the attribute by extracting the
important concepts from its business definition. Attributes in more than one model should
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have the same name and definition in all models.

Use a singular noun or singular noun phrase with qualifying adjectives that are meaningful
to the business.

Use Title Case.

Do not use a class word or its abbreviation by itself as an attribute name.

Do not use the word “Attribute” in the attribute name unless it is part of common business
terminology.

Attribute Name Structure
o
An attribute name begins with at least one Qualifier followed by a Class Word. Note
that conjunctions, verbs and other parts of speech are eliminated when they do not
affect the meaning of the name.
o
Class words describe the type of data identified by the attribute name. Examples
include: amount, code, date, indicator, name and number.
o
End the name with an approved class word that best categorizes the attribute.
Class words may also give an indication of the data type and possible values of the
attribute, e.g. an indicator is always a single alphanumeric character with only 2
possible values other than Null, ‘Y’ or ‘N’. 4
o
Units of Measure describe the quantity that was measured such as height or
volume.
o
4
Objects are used for program objects, images, sounds and videos.
See Appendix for details on Class Words.
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Examples of logical attribute names and their components:
QUALIFIERS
CLASS WORDS
MODIFIER
PRIME WORD
KEY WORD
Automobile
Acquisition
Date
Insurance
Company
Name
Payment
Status
Code
Valid Driver
License
Indicator
Vehicle Engine
Capacity
Accident
Photograph
UNIT OF MEASURE OBJECT
Cubic Centimeters
Image Jpeg
Relationship Naming Guidelines

The relationship name should be a verb or a verb phrase in third person singular form, i.e.
a verb form that is appropriate for a singular occurrence of the entity. This verb or verb
phrase should be an active verb in the parent to child direction and a passive verb phrase
in the child to parent direction. When used with the cardinality and optionality information,
the verb or verb phrase allows the relationship to be read as bi- directional English
sentences. For example: A POLICY covers zero, one or many EXPOSURE(S). An
EXPOSURE is covere d by one and only one POLICY.
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
Do not include words that convey cardinality or optionality in the verb phrase – words such
as ‘may’, ‘must’, ‘one and only one’ or ‘one or many’ are derived from the relationship
symbols.

Avoid using generic or vague words and phrases such as ‘is’, ‘has’, ‘consists of’, ‘relates
to’, ‘associated with ‘, etc.
Relationship Standards
A relationship describes the precise business rules governing the association between two
entities and facilitates the identification of foreign keys and referential integrity rules that may be
required in the database design.


The minimum components that must be specified for each relationship are:
o
Name – a verb or verb phrase from parent to child
o
Optionality rules
o
Cardinality rules
o
Qualification as an identifying or non- identifying relationship
Many- to- many relationships are desirable in Conceptual Data Models but should always
be resolved with an associative entity in a Logical Data Model even if the associative entity
has no attributes other than the keys.

Investigate all mandatory one- to- one relationships because usually the two entities are in
fact one entity.

Eliminate circular relationships because they cause problems establishing proper data
dependency sequences. They usually result from an incorrect or misunderstood business
rule.
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
Eliminate redundant relationships that consist of two dependency paths between the same
two entities. One of the paths is a direct relationship between the entities; the other uses a
non- direct path that involves other entities. These redundant relationships may lead to
problems with database consistency.

Carefully review multiple relationships between the same two entities as they tend to
represent process logic and may introduce conflicting cardinalities. If the multiple
relationships are created to document roles, a better solution may be to create a role entity
with appropriate subtypes.
Super-types and Sub-types
Super- types and sub- types can be the result of either a generalization process – bottom- up – or
a specialization process – top- down. The result is a super- type (parent) that contains attributes
that are shared by all subtypes and a sub- type (child) that inherits all the shared attributes from
the super- type but also has unique attributes of its own.

A sub- type has an ‘is a’ relationship to its super- type. Sub- types are not ‘composed of’
relationships.

Super- types and sub- types clarify complex business rules and constraints between
entities.

The super- type and sub- type have an exclusive OR relationship. An instance of the
super- type can be an instance of only one of the sub- type entities.
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An example of super- types and sub- types:
INSURED OBJECT
Insured Object Identifier
Geographic Location ID (FK)
VEHICLE
HOME
Insured Object Identifier (FK)
Insured Object Identifier (FK)
Registration State Code (FK)
MOTORCYCLE
RECREATIONAL VEHICLE
Insured Object Identifier (FK)
Insured Object Identifier (FK)
AUTOMOBILE
Insured Object Identifier (FK)
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Entity Keys
A key identifies specific occurrences of an entity. They can be simple, consisting of a single
attribute, or they can be composite, consisting of two or more attributes.

A Candida te Key uniquely identifies occurrences of an entity. There may be more than
one candidate key for an entity. Candidate keys are not usually recorded in the logical
data model because they become either a primary key or an alternate key.

A Primar y Key is a single candidate key selected as the ‘primary’ unique identifier for
the entity.
o
The primary key must be stabl e for a relational data model. If the value were to
change over time, the result could be either a non- unique key value or multiple key
values for one instance of an entity. Either situation could cause ambiguous or lost
data, system crashes or difficult update processes.
o
The primary key should be defini tiv e because it uniquely identifies an instance of
the entity and thus no instance can be added to the entity until its identity is fully
known. The primary key cannot be nullable or contain nullable components.
o
The primary key should use the m i n i m a l number of attributes required to define a
unique instance of the entity. A concise key has advantages in the physical
database such as smaller indexes and foreign keys.

An Al terna te Key is any candidate key not selected as the primary key of an entity.
Alternate keys are not usually recorded in the logical model but may become indexes in
the physical model. Alternate keys are usually unique but are not required to be.

A Surroga te Key consists of a single attribute created for the sole purpose of uniquely
identifying an instance of an entity. Natural keys consist of attributes that ‘naturally’ belong
to each occurrence of the entity. Surrogate keys are identifiers that contain no inherent,
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embedded data about the entity. That is to say, they are always non- intelligent keys.
Surrogate keys are usually a numeric attribute whose value can be generated
automatically either as a sequential number or a random number. Synonyms for a
surrogate key include: ar tificia l ke y, syn the tic ke y, arbi trar y ke y, and sys tem- genera te d ke y.

A Foreig n Key is a primary key of one entity (the ‘parent’ or independent entity) that is
duplicated in a separate, related entity (the ‘child’ or dependent entity). A foreign key is not
required to be unique within the child entity. A foreign key that is part of a composite
primary key in the child entity is known as an identifying or primary foreign key. Attributes
in a non- identifying foreign key become non- key attributes in the child entity.
Dimensional Data Modeling
There are dimensional data modeling concepts such as the grain of the model, conformed
dimensions, and diagramming layouts that deserve coverage in a standards document dedicated
to dimensional modeling. The next few paragraphs talk about which parts of the Relational Data
Modeling Standard apply to the Dimensional Modeling Standards and which do not.
Relational Data Models are designed to support operational databases that capture complex
information accurately. They deliver stable, flexible data structures that are easily navigated and
can answer unanticipated questions. Dimensional Data Models are designed to support reporting
and business analytics databases. They deliver simple, high- performance queries that answer a
set of anticipated questions.
Although Relational and Dimensional Data Models serve different purposes, they share many of
the same standards. Most importantly, they both use the Model Driven Architecture approach.
Also, the Logical Object, Entity, and Attribute Definition and Naming Guidelines apply to both
styles of modeling. They are both Entity Relationship diagrams and both use the same IE
modeling syntax. The Relationship Standards also apply to both though in practice relationship
names are not used as often in Dimensional models as they are in Relational.
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Dimensional models are a denormalized design. Super- types and sub- types would be merged.
Their diagramming layouts often use a star schema design and occasionally a snow- flaked
design so their Diagramming Layout Guidelines are different from the Relational model.
Appendix
Class Words
The three tables below enumerate approved class words which come in three flavors: key words ,
u n i ts of m e a s u r e and objects . Each class word has a standard abbreviation, definition, and
associated logical data type. The example is a typical column name and data value.
Key Word
Abbreviation Definition
Logical
Example
Datatype
Amount
AMT
A quantity of money.
NUMERIC
Code
CD
STRING
Count
CNT
Date
DT
Description
DSCR
Letters and numbers used
for brevity to identify
something.
A numeric count or
calculated quantity of
anything other than
money, used when no unit
of measure applies.
Time stated in terms of
year, month and day.
A statement that
represents something in
words.
Identifier, ID,
Identification,
Identity
Indicator
ID
Data that serves to
uniquely identify one item
in a group
Data that can have only
one of two values other
than NULL: Y(es) or N(o).
A set of characters
normally printed or
displayed as one
horizontal row.
A word or words by which
a thing is designated and
STRING or
IND
Line
LN
Name
NM
Logical Relational Data Modeling Standard
Policy Face
Amount = 1,200.0
Sales Office Code
= AR11
NUMERIC
Active Employee
Count = 41,256
DATE
Disability Date =
2002/4/5
Policy Change
Reason Description
= “Match coverage
to changed income”
Employee ID =
0123456
STRING
NUMERIC
STRING
(1 character)
Auditing Approval
Indicator = Y
STRING
First Address Line
= “451 MAIN ST”
STRING
Person Full Name =
“Sammy Somerset”
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Number
NUM
Objects
See Object
list below
Percent
Percentage
PCT
Text
TXT
Time
TM
Timestamp
Units of
Measure
TS
See Unit of
Measure list
below
distinguished from others.
Normally numeric data
used to identify ordinal
position or to distinguish
between items in a set.
When numeric, it must
always be a whole
number.
Binary Objects, such as
program objects, images,
sounds, or videos.
Numeric data specifying a
portion or share out of
each 100 units. (75 units
out of 100 is 75 percent
(%). Percent values are
multiplied by 0.01 in order
to facilitate customary
processing. In the
example, 75 percent
would be stored as
0.7500 but displayed as
75.00 %.)
Data having relatively
undefined content and
arrangement such as a
note, comments or an
explanation
Time stated in terms of
hours, minutes and
seconds
Time stated in terms of
year, month, day, hours,
minutes, seconds and
fractions of seconds.
Identifies an instant in
time.
All units of measure, e.g.
Feet, Months, Miles,
Centimeters.
Logical Relational Data Modeling Standard
STRING or
NUMERIC
Arrival Sequence
Number = 5
STRING
NUMERIC
Sales Closure
Percentage = .7500
STRING
Audience Comment
Text = “Enthusiastic
and attentive”
TIME
Check-In Time =
8:45 AM
TIMESTAMP
Transaction
Timestamp =
20021203134516.8
72
NUMERIC
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Unit of Measure
Abbreviation
Beats per Minute
BPM
Centimeters (Centimetres)
CM
Cubic Centimeters (Centimetres)
CC
Days
DAY
Degrees
DEG
Feet
FT
Grams
G
Horsepower
HP
Hours
HR
Inches
IN
Kilograms
KG
Kilometers (Kilometres)
KM
Kilometers (Kilometres) per Hour
KMH
Liters (Litres)
L
Meters (Metres)
M
Miles
MILE
Miles Per Hour
MPH
Millimeters (Millimetres)
MM
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Minutes
MIN
Months
MO
Ounces
OZ
Pounds
LB
Units. “Units” is a generic Unit of
UNIT
Measure (UOM) used when data with
different UOM will be stored in a
common column. In this case there
must be a companion code column
containing a UOM abbreviation
indicating the UOM of the Units value.
Weeks
WK
Years
YR
Object Type
Object Class
Abbreviation
C++
Program Object
OBJ_C
PowerBuilder
Program Object
OBJ_PB
SmallTalk
Program Object
OBJ_ST
Bitmap
Image
IMG_BMP
Gif
Image
IMG_GIF
Jpeg
Image
IMG_JPG
Rav
Sound
SND_RAV
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