Lecture 3 : Hard Systems Modelling UFCE8V-20-3 Information Systems Development SHAPE Hong Kong 2010/11

Hard Systems Methods Rooted in systems thinking and draws on systems concepts Basis of most I.S. and I.T. design methodologies. For example… – Structured Systems Analysis and Design Methodology (SSADM), Yourdon etc. Takes a functional view of the organisation 2

Underlying Assumptions There is a problem, the nature of which is agreed There is general agreement about the nature of a solution The means of solving the problem involve the provision of a new or redesigned computer based information system The problem solving process involves implementing formal modelling techniques within the framework of a methodology 3

Characteristics Usually a project life cycle based approach. For example… – Requirements analysis Model existing system Look for weaknesses Create model of improved system – Designing – Constructing – Testing – Implementation – Maintenance Clear instructions and procedures for carrying out each stage 4

Characteristics (2) – Strengths Distinct phases with “deliverables” Can evaluate cost v benefits Supports project management Generates comprehensive documentation May be supported by Computer Aided Systems Engineering (CASE) tools Historically has been the “industry standard” Methods seen as either “data driven” or “process driven” 5

Characteristics (3) - Weaknesses Unambitious system design Failure to meet the needs of management Instability Inflexibility User dissatisfaction Problems with documentation Lack of control Incomplete systems Application backlog Maintenance workload Problems with the “ideal” approach 6

Data Flow Diagrams Process driven technique Used for modelling information processing Elements are… – External entities – Data stores – Processes – Data Flows 7

Data Flow Diagram (Context-Level 0) 8

The top level diagram (level 0) models the whole system as a single process. The diagram identifies the following aspects of the system: - System boundary All external entities Related input and output flows 9

Data Flow Diagram (Level 1) 10

Simple Example 11 Student Check Course Availability Check Applicant Qualification Courses Applications D1 D2 Qualifications Application Details Application Accept/Decline Enrolment Course Course availability 1 2 Enrolment

Data Flow Diagram (Level 1) The level 1 diagram gives an overview of the whole system. The diagram identifies the following aspects of the system: - Major system processes Data flows between the processes Data stores 12

Resolution Levels Modelling a situation of any complexity can lead to huge models! The solution is to create hierarchies or “levels of resolution” Top more general – lower levels more detailed Created by breaking a single element at an upper level into a complete diagram at a lower level Guideline: 7 +/- 2 elements on a single model 13

Data Flow Diagram (Level 2) 14

Data Flow Diagram (Level 2) Level 2 diagrams showing constituent system processes. The diagram identifies the following aspects of the system: - External entities to a specific process Specific system processes Data flows in and out of each process 15

Other modelling – Entity Attribute Relationships (required for assignment) The following entity attribute diagram models the relationships between each database system entity. The diagram identifies the following aspects of the system: - Entity name Items about which the system needs to store data Internal structure of the entities Relationships between entities that are significant to the system 16

Entity Attribute Relationship Diagram (from normalised data) 17

Other modelling – Entity Life Histories Entity Life History Diagrams The following entity life history diagram models the movement of data through the system at various levels of detail. This diagram identifies the following aspects of the system: - Event sequencing Event iteration Event order and timing 18

19 Entity Life History

Summary Structured methods provide a framework to: – Aid communication – Provide a set of techniques that standardises an approach and is proven – Identifies errors – Eliminates ad-hoc problem solving – Ensures sufficient effort is put into the analysis stage. 20