1PalGov © 2011

أكاديمية الحكومة اإللكترونية الفلسطينية

The Palestinian eGovernment Academy

www.egovacademy.ps

Session 3.2

Mandatory Rules

Tutorial 1: Data and Business Process Modeling

Prof. Mustafa Jarrar

Sina Institute, University of Birzeit

mjarrar@birzeit.edu

www.jarrar.info

Reviewed by

Prof. Marco Ronchetti, Trento University, Italy

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About

This tutorial is part of the PalGov project, funded by the TEMPUS IV program of the

Commission of the European Communities, grant agreement 511159-TEMPUS-1-

2010-1-PS-TEMPUS-JPHES. The project website: www.egovacademy.ps

University of Trento, Italy

University of Namur, Belgium

Vrije Universiteit Brussel, Belgium

TrueTrust, UK

Birzeit University, Palestine

(Coordinator )

Palestine Polytechnic University, Palestine

Palestine Technical University, PalestineUniversité de Savoie, France

Ministry of Local Government, Palestine

Ministry of Telecom and IT, Palestine

Ministry of Interior, Palestine

Project Consortium:

Coordinator:

Dr. Mustafa Jarrar

Birzeit University, P.O.Box 14- Birzeit, Palestine

Telfax:+972 2 2982935 mjarrar@birzeit.edu

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© Copyright Notes

this material, or part of it, but should properly useEveryone is encouraged to

(logo and website), and the author of that part. cite the project

in any form or by any reproduced or modified No part of this tutorial may be

from the project, who have the full written permissionmeans, without prior

copyrights on the material.

Attribution-NonCommercial-ShareAlike

CC-BY-NC-SA

This license lets others remix, tweak, and build upon your work non-

commercially, as long as they credit you and license their new creations

under the identical terms.

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Tutorial Map

Topic Time

Module I: Conceptual Data Modeling

Session 0: Outline and Introduction

Session 1.1: Information Modeling 1

Session 1.2: Conceptual Data Modeling using ORM 1

Session 1.3: Conceptual Analyses 1

Session 2: Lab- Conceptual Analyses 3

Session 3.1: Uniqueness Rules 1.5

Session 3.2: Mandatory Rules 1.5

Session 4: Lab- Uniqueness & Mandatory Rules 3

Session 5: Subtypes and Other Rules 3

Session 6: Lab- Subtypes and Other Rules 3

Session 7.1: Schema Equivalence &Optimization 1.5

Session 7.2: Rules Check &Schema Engineering 1.5

Session 8: Lab- National Student Registry 3

Module II: Business Process Modeling

Session 9: BP Management and BPMN: An Overview 3

Session 10: Lab - BP Management 3

Session 11: BPMN Fundamentals 3

Session 12: Lab - BPMN Fundamentals 3

Session 13: Modeling with BPMN 3

Session 14: Lab- Modeling with BPMN 3

Session 15: BP Management & Reengineering 3

Session 16: Lab- BP Management & Reengineering 3

Intended Learning ObjectivesModule 1 (Conceptual Date Modeling)

A: Knowledge and Understanding

11a1: Demonstrate knowledge of conceptual modeling notations and concepts

11a2: Demonstrate knowledge of Object Role Modeling (ORM) methodology.

11a3: Explain and demonstrate the concepts of data integrity & business rules

B: Intellectual Skills

11b1: Analyze application and domain requirements at the conceptual level,

and formalize it using ORM.

11b2: Analyze entity identity at the application and domain levels.

11b4: Optimize, transform, and (re)engineer conceptual models.

11b5: Detect &resolve contradictions & implications at the conceptual level.

C: Professional and Practical Skills

11c1: Using ORM modeling tools (Conceptual Modeling Tools).

Module 2 (Business Process Modeling)

A: Knowledge and Understanding

12a1: Demonstrate knowledge of business process modeling notations and concepts.

12a2: Demonstrate knowledge of business process modeling and mapping.12a3: Demonstrate understand of business process optimization and re-engineering.

B: Intellectual Skills

12b1: Identify business processes.

12b2: Model and map business processes.

12b3: Optimize and re-engineer business processes.

C: Professional and Practical Skills

12c1: Using business process modeling tools, such as MS Visio.

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Session ILOs

After completing this session students will be able to:

11a3: Explain and demonstrate the concepts of data integrity and

business rules.

11b1: Analyze application and domain requirements at the

conceptual level, and formalize it using ORM.

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Conceptual Schema Design Steps

1. From examples to elementary facts

2. Draw fact types and apply population check

3. Combine entity types

4. Add uniqueness constraints

5. Add mandatory constraints

6. Add set, subtype, & frequency constraints

7. Final checks, & schema engineering issues

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Mandatory Versus Optional

each Patient Has at least one PatientName

Any problem with this table?

By default, all roles are optional.

How can we say that a role is mandatory?

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Mandatory Roles

Mandatoriness is implied with Value Types

(by default).

For example: it is assumed that “each

PhoneNr is for at least one Patient”, because

PhoneNr is a Value Type.

??

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Mandatory Definition

Role r is mandatory (for the population of A)

Each instance of type A that is recorded in the database is

also recorded to play r

pop( r) = pop( A)

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Mandatory Roles

How can we decide to make a role mandatory?

Recall our discussion on

(Conceptual data modeling) versus (conceptual modeling)

If a role is mandatory in the real world, it may be optional in the model.

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Disjunctive Mandatory

How to say that it is mandatory for each member to (PlaysFor | Coaches)

a team, or both. We cannot have both roles optional, at least one role

should be played.

Disjunctive mandatory constraint:

Each Member coaches or plays (or both).

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Disjunctive Mandatory

Alternative notations for disjunctive mandatory role constraint.

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Definition of Disjunctive Mandatory

Disjunctive mandatory role constraint

The inclusive disjunction of roles r1...rn is mandatory for A

i.e., each member of pop(A) plays r1 or r2 ...or rn (or all)

i.e., each member of pop(A) plays at least one of r1 … rn

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Another Example

Each person must be a parent or a child.

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Back to Uniqueness

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External Uniqueness

How to say that the combination of (Discipline, YearLevel, SerialNr) is

unique for each subject?

The Subject code might be generated from this combination

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Another Example

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Reference Schemes

The identity is achieved if an entity has a mandatory and unique role

In data modeling, each entity (i.e., Object Type) must have an identity.

This is not important (i.e. implicit) in ontology modeling.

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Discussion

Summarize what you learned? And what you think about it?

Compare what you learned with EER and UML?

Questions & Suggestions?

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References

1. Information Modeling and Relational Databases: From Conceptual Analysis to Logical Design, Terry Halpin (ISBN 1-55860-672-6) – Chapter 5.