Information Technology IMS 5024 Information Systems Modelling Data Modelling.

Information Technology

IMS 5024 Information Systems Modelling

Data Modelling

School of Information Management & Systems

7.2

Content

• Second assignment• Pitfalls revisited• Nature of data modelling• Tools/Techniques used in data modelling• Place in ISD• Evaluation of data modelling• Reading list

NB. Not all slides in this lecture will be discussed


7.3

Assignment two – OO modelling

• Due Monday 4th of October – week 11• Undertake in pairs from the same tute

group.• Individual submissions accepted

• Decide this week!


7.4

Assignment Pitfalls

• Not starting early• Not confirming your understanding of the case

and the requirements with me and Clyde• Not starting early• Not integrating the separate elements of the

models• Not starting early• Assuming that Clyde and I do not coordinate

our marking


7.5

Data modelling describes:

• Structure• Meaning• Relationship

of data


7.6

Data modelling help us to grasp:

• static data in the organisation

• fundamental building blocks of the system

• different view of data from that of process modelling


7.7

Techniques used in Data Modelling

• Normalisation• Data Dictionary• Entity relationship diagrams

• What difference?• Use all?


7.8

Entity

• Entity – thing of interest to the business

• Identifying an entity is subjective• Entities can be:

• Real eg product• Abstract eg quota• Event remembered eg sale• Role played eg employee

Employee


7.9

Relationship

• Relationship Between entities

• Cardinality (eg. One to many, one to one ect.)

• Degree of relationship (Unary, Binary, Ternary)

DepartmentEmployee


7.10

Notation conventions

• There are several - • but ER concepts are consistent

• Entity

• Cardinality and existential status

• Degree


7.11

PATIENTHISTORY

PATIENT

PROJECT

EMPLOYEE

Mandatorycardinalities

Optional and mandatorycardinalities

Optionalcardinalities

HasIs

assigned to

Is married

to

Examples of Cardinalities

PERSON


7.12

Mandatory 1 cardinality

Many cardinality (1,2 …m)

Optional (0 or 1) cardinality

Optional (0 or many) cardinality

Relationship Cardinality Summary


7.13

• Also called a recursive relationship

One to manyOne to one

Unary Relationship

EmployeeIs married to

Person Manages


7.14

• A binary relationship is a relationship between instances of two entity types

PROJECT

EMPLOYEE

SALESORDER

CUSTOMER

ITEM

SUPPLIER

One to one One to many Many to many

Binary Relationship

Leads Places Supplies

Supplied byPlaced byLead by


7.15

• A ternary relationship is a relationship between instances of three entity types.

PART

suppliesVENDOR WAREHOUSE

Ternary Relationship


7.16

Attributes

• Individual components of information that characterise an Entity – its constituent data

• Types:• derived,

• multi-valued,

• composite,

• simple


7.17

Example of attributes

EMPLOYEE

Emp-no

Name Address

Skill


7.18

Normalisation

• the process of identifying the “natural” groupings of attributes• remove redundancy and incompleteness

• a bottom up process

• relies on Set Theory – well researched


7.19

Determining columns

• One fact per column• Hidden data• Derivable data• Determining the key

11352466 2358 CA F 24 3145

11852323 2358 CA F 24 3128

18702256 3320 CA M 12 3163

16554471 3324 CA F 24 3175


7.20

• Basic Normalisation is most often accomplished in three stages (these are the three basic normal forms)

First Normal Form

Second Normal Form

Third Normal Form

Unnormalised table

Remove repeating groups

Remove partial dependencies

Remove transitive dependencies

Steps in Basic Normalisation


7.21

First normal form

Step 1: Remove the repeating group• Why are repeating groups a problem?• Determine the key for the new group.

Order-Item (Order#, Customer#, (Item#, Desc, Qty))

Order-Item (Order#, Item#, Desc, Qty)

Order (Order#, Customer#)


7.22

Second and Third normal forms

• Eliminate redundancy• Determinates – one or more columns

(attributes) which determine other column values


7.23

Second and Third normal form procedure

• Identify any non-key determinates• Establish a separate table for each

determinate and the columns it determines

• Name the new tables• Remove the determined columns from

the original table


7.24

Third normal form

A table is in third normal form if the only determinant(s) of non-key columns is (are) the primary key determinant(s)


7.25

Advanced normalisation

• A set of tables can be in 3NF and still not be fully normalised

• Further stages of normalisation are BCNF, 4NF, 5 NF and Domain key NF

Refer to Date, C.J. (1990) An Introduction to Database Systems, Volume 1. 5th edn. Addison-Wesley Publishing Company, MA. pp543-557


7.26

Higher Normal forms

• Occur infrequently• Most tables in 3 NF are already in BCNF,

4NF and 5 NF• Data in 3NF but not in 5NF has

• Redundancy

• Insert/update/delete anomalies

• Difficulty in storing facts independently


7.27

Thinking in Data modelling

• Hard Vs Soft ??• Perspective

• Objective vs Subjective

• Nature of the organisation


7.28

Evaluation of Data modelling

Problem oriented Product oriented

Concep-tual

Structured analysis

Entity relationship modelling

Logical construction of systems

Modern structured analysis

Object oriented analysis

Structured design

Object oriented design

Formal PSL/PSA

JSD

VDM

Levels of abstraction

Stepwise refinement

Proof of correctness

Data abstraction

JSP

Object oriented programming


7.29

Advantages of Data modelling

• Data model is not computer oriented (agree??) • Model understandable by technologist and users• Does not show bias• UoD can vary (whole organisation or department)• Readily transformable into other models• Different data analysis techniques• Data modelling is rule-based


7.30

Disadvantages

• Does not encourage or support user participation

• Your view on the organisation – people or data

• The idea that the model is THE model• Subjective view• One-side to data• Others??


7.31

Advantages of Normalisation

• Rid the data of redundancy and other problems

• Very well researched• Math basis for normalisation


7.32

Disadvantages of normalisation

• Does not encourage or support user participation

• Your view on the organisation –people or data

• One-side to data• Can be done mechanistically without

thought• Others??


7.33

Reading for next week

• Johnstone, M.N., McDermid, D.C. (1999). Extending and validating the business rules diagram method. Proceedings of the 10 th Australian Conference on Information Systems.

• Chapter 2 of Curran, Keller, Ladd (1998). SAP R/3 the business blueprint: Understanding the business process reference model. Prentice Hall.

Information Technology IMS 5024 Information Systems Modelling Data Modelling.

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