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IMS 5024 Information Systems Modelling. Data Modelling. 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. - PowerPoint PPT Presentation
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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
School of Information Management & Systems
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!
School of Information Management & Systems
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
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7.5
Data modelling describes:
• Structure• Meaning• Relationship
of data
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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
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7.7
Techniques used in Data Modelling
• Normalisation• Data Dictionary• Entity relationship diagrams
• What difference?• Use all?
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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
School of Information Management & Systems
7.9
Relationship
• Relationship Between entities
• Cardinality (eg. One to many, one to one ect.)
• Degree of relationship (Unary, Binary, Ternary)
DepartmentEmployee
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7.10
Notation conventions
• There are several - • but ER concepts are consistent
• Entity
• Cardinality and existential status
• Degree
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7.11
PATIENTHISTORY
PATIENT
PROJECT
EMPLOYEE
Mandatorycardinalities
Optional and mandatorycardinalities
Optionalcardinalities
HasIs
assigned to
Is married
to
Examples of Cardinalities
PERSON
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7.12
Mandatory 1 cardinality
Many cardinality (1,2 …m)
Optional (0 or 1) cardinality
Optional (0 or many) cardinality
Relationship Cardinality Summary
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7.13
• Also called a recursive relationship
One to manyOne to one
Unary Relationship
EmployeeIs married to
Person Manages
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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
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7.15
• A ternary relationship is a relationship between instances of three entity types.
PART
suppliesVENDOR WAREHOUSE
Ternary Relationship
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7.16
Attributes
• Individual components of information that characterise an Entity – its constituent data
• Types:• derived,
• multi-valued,
• composite,
• simple
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7.17
Example of attributes
EMPLOYEE
Emp-no
Name Address
Skill
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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
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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
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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
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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#)
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7.22
Second and Third normal forms
• Eliminate redundancy• Determinates – one or more columns
(attributes) which determine other column values
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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
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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)
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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
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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
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7.27
Thinking in Data modelling
• Hard Vs Soft ??• Perspective
• Objective vs Subjective
• Nature of the organisation
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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
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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
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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??
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7.31
Advantages of Normalisation
• Rid the data of redundancy and other problems
• Very well researched• Math basis for normalisation
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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??
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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.
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