Www.sims.monash.edu.au Information description – data dictionary Introduction to data modelling IMS9300 IS/IM FUNDAMENTALS

www.sims.monash.edu.au

Information description – data dictionaryIntroduction to data modelling

IMS9300 IS/IM FUNDAMENTALS

www.sims.monash.edu.au/subjects/ims93002

Data Modelling

• Focus on the information aspects of the organisation

• In a business environment many tasks share the same data

• The database/ data warehouse is a common asset and corporate resource


Conceptual Data Modelling

• A conceptual data model is a representation of organisational data

• Captures the structure, meaning and interrelationships amongst the data

• Independent of any data storage and access method, DBMS, platform issues

• Occurs in parallel with other systems analysis activities


• Identification of information requirements• Allows integration of data across the

organisation and across applications• Helps eliminate problems of data

inconsistency and duplication across the organisation



• Techniques;– Entity Relationship (ER) modelling

– Data dictionary

• Good modelling techniques are supported by rigorous standards and conventions to remove ambiguity and aid understanding



• the data dictionary is a database or repository of information about objects identified during systems development

• every object (and each of its components) must have a definition in the data dictionary

• the data dictionary is a major source of documentation about the information system

• the data dictionary must contain precise definitions of all components of all data flow diagrams

Data Dictionary


Data Dictionary -data element entry

•ACADEMIC CONSULTANCY

DATA ELEMENT: Product code

•Alias: Inventory number, product number•Description: Number to identify and differentiate each product held in warehouse•Values: Must be a positive integer•Range: 00001 to 99999

AUTHOR: David Ross

DATE: 14 Oct 2002


Data Dictionary -data flow entry


DATA FLOW: Sales order

•Alias: Customer order•Description: Request for product from customer•Composition: sales order =sales order no. +

sales order date +

customer number +

[account customer

cash customer] +

customer name +

customer address +

(customer telephone no) +

{item no + item desc + item price + item qty} +

sales order total amount

AUTHOR: David Ross

DATE: 03 July 2002


Data Dictionary -data store entry


DATA STORE: Sales

•Description: Store of all pertinent data about product sales made to customers•Composition: sales = date+

({product}

product quantity)+

sales total

{customer}

AUTHOR: David Ross

DATE: 10 Oct 2002


Data Dictionary Entries

• a data dictionary entry must be included for each – data flow

– data store

– higher level process

– primitive process

– external agent


Detailed Data Modelling

• To expand the conceptual data model, we need to identify and describe the details of the entities and relationships

• Attributes (data elements) of entities and relationships are identified

• The attributes should be independent of implementation technology

• Each attribute should represent a single “fact”• An organisation-wide perspective should be adopted to

ensure minimum redundancy and inconsistency and to facilitate data sharing


Attributes

• An attribute is a named property or characteristic of an entity that is of interest to the organisation

• Use an initial capital letter followed by lower case letters in naming attributes

• Each attribute name must be unique


Identifying Attributes

There are three main sources of attributes:• data to support essential user functions –

members_address; book_publisher

• data to support current operations – loan_commencement_date; amount_paid

• data to measure performance against objectives – purchase_date; loan_quantity


• examine all forms, documents, reports and files (computerised and manual) used in the current system

• check with users for accurate definitions of attributes

• ensure all attributes identified are still required in the new system

Identifying attributes


Documenting Attributes

• All attributes should be defined and described in the data dictionary

• Information should include:

namedescription

example valuesdomain of values

range of valuessynonyms

method of derivationvalidation constraintsentities which this attribute

describes


• Each attribute should convey a single fact

avoid embedding extra information in ranges of values

e.g. invoice-number

0000-1499 north-east region1500-2999 south-east region

3000-4499 central region

Attribute definition


The relational database model

• The relational database model represents data in the form of tables or relations

• Important concepts are:– relation

– primary key

– foreign key

– functional dependency


Example relation

• A relation generally corresponds to some real world object or concept of interest to the system (derived from an entity) e.g.:

Emp# Name Salary Dept

1247

1982

9314

Adams

Smith

Jones

24000

27000

33000

Finance

MIS

Finance

Employee

Employee (Emp#, Name, Salary, Dept)


Primary key

• An attribute or group of attributes which uniquely identifies a row of a relation

• Entity integrity (relational data base theory) requires that each relation has a non-null primary key

• Where several possible keys are identified, they are known as candidate keys - choose one to be the primary key

Employee (Emp#, Name, Salary, Dept)

Order-item (Order#, Item#, Qty-ordered)

E.g.


Well Structured Relations

• A well structured relation contains a minimum amount of redundancy and allows users to insert, modify, and delete rows in a table without errors or inconsistencies (known as “anomalies”)

• Three types of anomaly are possible:

– insertion

– deletion

– modification• Third normal form relations are considered to be well

structured relations


Entity Relationship Modelling

• Used for conceptual data modelling• Diagrammatic technique used to represent:

– things of importance in an organisation - entities

– the properties of those things - attributes

– how they are related to each other -

relationships


Entity

• Something of interest about which we store information

eg. EMPLOYEESALES ORDERSUPPLIER

• Often identified from nouns used within the business application

• Should be LOGICAL (not physical)


Entity Types and Instances

• An entity type is a classification of entity instances

eg BN Holdings ABC Engineering Acme Corp. Ltd.

SUPPLIER


Relationship

• Is an association between two classes of information – eg. customer and order

• We may wish to store additional information about the association itself – eg. volume of orders placed

• Often described in the form: entity + verb + entity eg. CUSTOMER places ORDER

• Separation of information into easily manageable collections (data tables) often necessitates mechanisms to link related collections of data


Representing Relationships

• We represent a relationship as a line between two entities

• The relationship is named by a meaningful verb phrase which should indicate the meaning of the association

• Relationships are bi-directional so naming each end of the relationship conveys more meaning

SUPPLIER ITEMsupplies


Cardinalities in Relationships

• The cardinality of a relationship is the number of instances of one entity type that may be associated with each instance of the other entity type

eg a CUSTOMER may place many ORDERs

an ORDER is placed by one CUSTOMER

an ITEM can appear on many ORDERs


Examples of Cardinalities

EMPLOYEE CUSTOMER SUPPLIER

PROJECTSALES

ORDERITEM

leads places supplies

One to One One to Many Many to Many


Nature of Relationships

We can indicate whether relationships are optional or mandatory:

• A customer MAY place many sales orders

• Each sales order MUST be placed by one customer

CUSTOMER SALESORDER

places

placed by


Nature of Relationships - Notations

EMPLOYEE

EMPLOYEE

MUST have a related record

May have a related record


Example ER model

MEMBER

FINE

LOAN

BOOK

takes outmakes

is for


Building a Basic ER Model

• Identify and list the major entities in the system

• Represent the entities by named rectangles

• Identify, draw, name, and quantify relationships • Indicate mandatory/optional nature of

relationships

• Revise for entity subtypes where appropriate


References

• Barker, R. (1989) CASE*METHOD Entity Relationship Modelling, Addison-Wesley, Wokingham UK. Chapters 4,5

• Hoffer, J.A., George, J.F. and Valacich, J.S., (1999)., Modern Systems Analysis and Design, (2nd ed), Benjamin/Cummings, Massachusetts. Chapter 10

• Whitten, J.L. & Bentley, L.D. and Dittman, K.C., (2001), Systems Analysis and Design Methods, (5th edn.), McGraw Hill Irwin, Boston MA USA. Chapter 5

Documents

Www.sims.monash.edu.au Information description – data dictionary Introduction to data modelling IMS9300 IS/IM FUNDAMENTALS