ITU - MDD – Modeling Techniques

ITU - MDD – Modeling Techniques
This presentation describes different modeling techniques. It has three basic purposes:
Give you an overview of some of the different types of models
Give you a set of tools to decide how to create a (good) model for a problem
Give you an overview of some of the different modeling techniques
This presentation is developed for MDD 2010 course at ITU, Denmark

What is Data Modeling all About?
It is primary a way to analyze and describe a problem domain in terms of the objects and relationships that are relevant for the domain
Often the result is also used to describe the functionality of the problem domain in terms of who does what (UML term: interaction diagrams)
The result is the abstract syntax for the problem domain
Especially relevant for all problems that includes technologies like
Object oriented languages like Java, C++ or C#
Non-trivial data structures
Relational databases
… which is just about 95% of all interesting problems

Agenda
Types of Models
Processes and Techniques
Model Normalization
Exercise

Types of Models
A question of abstraction!
Information Model or Conceptual Model
Describes the relationships between the important concepts of the model
Data Model, Logical Model or Domain Model (DOM)
Describes the corresponding relationships between the objects that are used to realize the concepts
Physical Model
Describes the logical model in a form that can be directly implemented in the target technology – e.g. Java or SQL
It is often possible to automatically transform a logical model to a physical model
Hibernate and JPA are examples of this
The different types of models is not related to meta levels – these models exists for M1-M3

Conceptual Models
The conceptual model
describes the things or objects of significance to a specific problem domain
The “ things ” are not necessarily physical objects
is meant to collect information, and characteristics of the objects in the domain
describes the most important associations between pairs of those things or objects of significance
is often used as the basis for the central use cases
It can very often be a good start to write down statements about the problem domain including the basic use cases before the model is started
To describe the human body:
A human BODY consists of a TORSO, normally connected with two LEGS, two ARMS, and a HEAD all called BODY PARTS
To describe entity-relationship diagrams
An ENTITY can refer to a number of other entities via RELATIONS. An entity can have ATTRIBUTES with DATA TYPES. REFERENCES between ENTITIES can be bi-directional. ENTITIES can inherit RELATIONS and ATTRIBUTES from a number of super-ENTITIES.

Conceptual Model (cont’) 0..1 Attribute Relation Entity Data Type 0..* 0..* 0..* attributeType opposite superTypes referenceType 0..* 0..* Body Part Torso 0..* ~ ~ 0..*

Logical Model
The logical model
typically refines the conceptual model
typically contains all the important data structures and classes from the application architecture along with their relations
is normally platform independent and can thus be implemented using all types of technologies (e.g. Java or SQL)
is often used as the basis for the detailed use cases
All design choices must be made as part of the logical model!
For large systems, the logical model exists in several layers with more detailed models for different parts of the base model

Logical Model (cont’)
Constraints and invariants can often be implemented in the abstract syntax (the model) as well as as dynamic constraints
To keep the model understandable, the “plain data” for entities is often kept is separate data dictionaries (including defined methods, etc for UML models) – this can make it relatively difficult to normalize the model as it is difficult to get a proper overview of the model
You should not necessarily expect to find all the conceptual concepts in the corresponding logical models!

Logical Model (cont’) Torso Body Part Torso 0..* ~ Leg Arm Head 0..2 Leg Arm Head 0..2 1 Torso 1 Leg Arm Head 1 1 X 2 X 2

Logical Model (cont’) ~ Named Element Model Element 0..1 Typed Element Classifier Structural Feature Attribute Reference Class Data Type 0..* 0..* 0..* type opposite superTypes 0..*

Physical Model
The physical model
normally closely mimics the logical model
adapts the logical model to the technical constraints of the target platform
adds or refines the information to be platform specific
E.g.
Data types are often more primitive – e.g. no enumerations or compound types in SQL
Bi-directional references are often resolved into multiple references
Additional performance specific data can be added in the form of indexes and storage specifications
B * A * Rel A * B *

Common Terms
Organization, Problem Domain or Universe – the area of interest, the “thing” that most be modeled
Entity – an object or concept from the problem domain – can also be data on relations or associations
Relation, Association or Reference – the connection between entities
Aggregation, Composition or Containment – a relation that implies some sort of ownership and shared life-cycle
Attribute – a data item on an entity
Key or Identifier – one or more attributes that are used to (uniquely) identify an entity
Structural Feature – common name for references and attributes
Entity-Relationship Diagram (ER diagram)

Model Notations
Entity-Relationship Model or Diagram (ER diagram)
The overall common term for all the notations
Also allows data attributes on relations – can always be converted to a simpler notation
UML 2 Class Diagram
OMG governed notation
Very rich
(I use a reduced UML notation when possible to remove as much “noise” from the diagrams as possible)
((Notations is the target of many religious discussions – the end result is usually the same though…))

Notations

Modeling Tools
Hand written on paper or a white board!
Eclipse Ecore Tools
UML tools such as IBM RAD and similar IDEs

The Typical Process
“ Modeling Techniques” are integrated and described in many different formal development process
Most processes encompass the following steps:
Identify Entities – typically corresponding to the objects in the problem domain
Identify Relations – typically according to the ownership or interaction between entities
Introduce inheritance
to collapse common behavior
to allow to-many references
Add attributes for all identifiers and specify constraints (especially cardinality of relations)
Apply generic model patterns and “normalize” the model to get the best balance between consistency and performance
Add all other “plain data” attributes
Practice, practice, practice… Sorry, not easy road here

Relations, References and Associations
Two basic groups of relations:
Aggregations, Composition and Containment
Plain References (sometimes “Reference Relations”)
Describes ownership and life-cycle
UML has two concepts
Aggregation denotes a weak “has a”
Composition denotes a strong “has a”
Shop Order * Customer Order Item Shop Item * * * * *

Techniques – One or More Relations Torso 0..2 Leg Arm Head 0..2 1 Torso 1 Leg Arm Head 1 1 X 2 X 2

Techniques – Inheritance Torso Body Part Torso 0..* Leg Arm Head 0..2 Leg Arm Head 0..2 1 Body Part Torso 0..* Type := “Leg”, “Arm, “Head”

Techniques – Attributes with Limited Value Set
E.g. Colors, Fonts, Vendors, Zip codes, Employees, Flight No
Model association as a string attribute
Model association as an enumeration attribute
Model association as a reference to a data entity (reference table)
Model association as a reference to different sub-types (inheritance)
Question: what is the meta level of the reference table?
Question: What is the associated cost of changing the application for each solution?
Very much related to the notion of normal forms

Techniques – Attributes with Limited Value Set Body Part Torso 0..* Type : String Body Part Torso 0..* Type := “Leg”, “Arm, “Head” Body Part Torso 0..* Body Part Type 0..* 1 Body Part Torso 0..* Leg Arm Head

Model Normalization
Model normalization is a process where certain types of redundancies are removed from a logical model
The primary aim is to avoid inconsistencies
The price is typically an increased performance/execution cost – especially for SQL based databases!
First normal form (1NF) – An entity type is in 1NF when it contains no repeating groups of data
Second normal form (2NF) – An entity type is in 2NF when it is in 1NF and when all of its non-key attributes are fully dependent on its primary key
Third normal form (3NF) – An entity type is in 3NF when it is in 2NF and when all of its attributes are directly dependent on the primary key
 Only one key attribute
Very often a model will go through a number of normalization/de-normalization cycles to find the right balance between consistency and performance

Model Normalization Example (0NF)

Exercises
We have a BOOK SHOP with a number of BOOKS on stock.
The BOOKS are sold to CUSTOMERS. A single ORDER can include many different BOOKS to a specific CUSTOMER.
Exercise I: Build a conceptual model for the book shop
What meta level does the model have (M0-M3)?
Exercise II: Build the corresponding data model for the book shop
What normal form does the model have? Can you make the model NF3? Is it worth it?
Exercise III: How must be models be changed if we need to track the price of BOOKS over time so we can draw diagrams with PRICE versus ORDERS for specific BOOKS

Syllabus (”Pensum”)
The reading materials for this lecture is in the form of the following three articles:
Wikipedia on Data Modeling - http://en.wikipedia.org/wiki/Data_modeling
Overview of the concepts behind data modeling
“ Data Modeling 101” by Scott Ambler - http://www.agiledata.org/essays/dataModeling101.html
Good introduction to the modeling process
“ UML 2 Class Diagrams” by Scott Ambler - http://www.agilemodeling.com/artifacts/classDiagram.htm
Good over the UML 2 Class Diagram Notation

ITU - MDD – Modeling Techniques

by Tonny Madsen on Nov 08, 2010

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ITU - MDD – Modeling Techniques — Presentation Transcript