The Art of Conceptual Modelling - is.informatik.uni-kiel.de · Conceptual Modelling Art SS 2012 B. Thalheim Introduction Modelling? Problems Concerns Model Modelling Act Principles

The Art of Conceptual Modelling

CAU@Kiel, Vorlesung SS 2012

WInf-BAppE: Selected Topics in Business Application Engineering (WInf-BAppE) (080001)

SS 2012

Bernhard ThalheimDr. rer.nat.habil.

Prof. @ Christian Albrechts University at Kiel, GermanyDepartment of Computer Science

Information Systems Engineering Group(∗) Kolmogorov Professor h.c. @ Lomonossov University Moscov, Russia

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ConceptualModellingArtSS 2012

B. Thalheim

Introduction

Modelling?

Problems

Concerns

Model

Modelling Act

Principles

Finally

Concept Topic

Content

Information

c⃝B. Thalheim

The Art of Conceptual Modelling - Survey

(1) Is there any general theory of conceptual models?

Far too many application areas; many approaches, viewpoints !!

(2) Restricting consideration to language-based models

Which universality we need?

(3) Models as artifact, image and prescription

Are we able to handle this variety?

(4) Purpose orientation of models

Can we derive model properties from the purpose?

(5) Semiotics as a language background

Which kinds of associations we might derive?

(6) Principles and theory

Is there any theory of models?

(7) Modelling acts and modelling workflows

Are we able to manage modelling at a SPICE level 3?

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B. Thalheim

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Today:Information systems development is

• development in the large taught almost nowhere• however everybody likes his/her languages and his/her understand-

ing - typically incompatible with everybody else

• might be based on database components and pattern of

schemata �� Our Background

• schema library consisting of more than 10.000 applications

largest with 95.000 types

• used for learning experiences from developers

• evolution, change, re-engineering of most applications at least after

6 month of existence

• migration, integration and collaboration projects

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Why this lecture?�� Is there any theory or technology of conceptual modelling?

Garcia-Molina, Ullman, Widom: ER/relational/other data models, infor-

mation integration

Hoberman: elements (entity, data elements, relationships), definition, subject

area, logical model, physical model, best approach, validate

Mannila, Raiha: ER/relational/other data models, design principles, con-straints, properties of relational schemas, mappings, transformations

Olive: elements (entity types, relationship types, constraints, special relationship

types), reification, generic relationship types, derived types, taxonomies, do-

main events, action request events, UML diagrams, metamodeling, MOF

Simsion: overview (what is modeling, normalisation, ER, subtypes/supertypes),

advanced data modeling (constraints, normalisation, identity, attributes, time

dimension, business rules, corporate data modelling

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Large and Complex Schemata: MDACIDOC

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Large and Complex Schemata: MDACIDOC Class Hierarchy

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Do we Need a Science of Modelling?

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Evolved and Problematic Schema

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Handling Large Schemata ThroughLayered Architectures

�� Layered Solution

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Models in Computer Science (> 50)�� How to obtain a general view?

... see next slide ...Constructionminimal

abstract /formal

model checking

mathematical

stochastic

cost-based, complexityanalytic

discretecontinuous

simulation

investigation

OSI-client-server

QuASAR

reference

(theoretisches) machine

computingcommunication

module/componentprogramming

architecture

model-driven architecturesmetawaterfall

spiralphase

development process

conceptual modelmodel world

model presentation

layer

basis/groundmodel class

understanding

model

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Models in Computer Science (> 50)�� How to obtain a general view?

eventstatefunctional

processcomponentobject

interface

storyboardinteractions

in various languagesworkflow

conceptual

logicdatabase

informationsystem

task

usage models depending on user profilesmetaUnified modelling language

modelling languages

system

constructionmodels

�� Yes, we can to obtain a general view!!!��

��(a) Construction; (b) Explanation; (c) Quality assurance; (d) ...

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Models in Computer Science (> 50)�� Can we learn from other models?

construction theory support

...

staticdynamic

formalinformal

micro macro...

authoraddressee

syntacticsemantic

Concernscovered byCS models

�� Yes, we can learn from other models!!!

��

��(α) Combination; (β) Separation; (γ) Model construction; (δ) Theoriy; (η) ...

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Models we are Not Going to Consider

Combinatoric-regular polyeder

(Dycks map)the usual joke

Model used as a form for pro-

duction

Models for replication:

Nofretete

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Modelling

Arts: working of plastic materials by hand to build up form

Mathematics: method of scientific investigation of systems

no necessity to construct an actual physical model of the system

mathematical model: a description of the system in some algorithmic language

divided into individual parts and the state of each part described by some

system of parameters

description of the relationships between the separate parts

Cybernetic systems: self-improvement, self-teaching and self-development

to model

• to plan or form after a pattern or shape

• to make into an organization (as an army, government, or parish)

• to produce a representation or simulation to model a problem

• to construct or fashion in imitation of a particular model

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General Principles and Properties ofModelling��

��(D, C, (R, ρ, θ, Ψ), G, W)

• things D under consideration

• concepts C described in a language L

• relationship R ⊆ D × C with

• restrictions ρ to its applicability

• modality θ or rigidity of the relationship

• confidence Ψ in the relationship

• agreed upon within a group G within a culture C

• valid in a certain world W�� Principle of Abstraction

we may however ‘invent’ concepts (distortion) and ‘restrict’ the model

(extension of properties)

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��(D, C, (R, ρ, θ, Ψ), G, W)

mapping property: have an original,

truncation property: the model lacks some of the ascriptions

made to the original,

pragmatic property: the model use is only justified for particular

model users, tools of investigation, and period of time,

extension property: represent judgments which are not observed

for the originals

distortion property: for improving the physical world or for inclu-

sion of visions of better reality

Stachowiak: 1-3

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��(D, C, (R, ρ, θ, Ψ), G, W)

(6) Idealisation property: Modelling abstracts from reality by scop-

ing the model to the ideal state of affairs.

(7) Carrier property: Models use languages and are thus restricted

by the expressive power of these languages.

(8)Added value property: Models provide a value or benefit based

on their utility, capability and quality characteristics.

(9) Purpose property: Models and conceptual models are governed

by the purpose. The model preserves the purpose.

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Criteria for Appropriateness of an Artifactto Become a Model

(1) The adequacy of a model defines its potential for the goals. Adequacy is given by the

similarity of the model with its origin in dependence on its goal, the regularity for the

application (within a well-founded system that uses rules for derivation of conclusions), the

fruitfulness (or capacity) for goals, and the simplicity of the model through the reduction to

the essential and relevant properties in dependence on the goal.

(2) A model is fit for its purpose if it is usable for the purpose, suitable within the given

context and for the prescribed purposes, robust against small changes in the parameters,

accurate to the level of precision that is necessary for the purpose, and compliant with the

funding concepts, application context and meta-model.

The model must be testable and, if false, it can be disconfirmed by a finite set of observations

(finitely testable) and by any of superset of these observation (irrevocably testable).

(3) The usefulness for deploying is given by effectiveness for complete and accurate sat-

isfaction of the goal, understandability for purposeful deployment of the model by users,

learnability of the model within the deployment stories, reliability and a high degree of pre-

cision of the the model, and efficiency of the model for the function of the model within the

application. testability

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Qualities of a Model�� Mainly Qualities of Use

Static qualities for a model

Development quality: pervasiveness, analysability, changeabil-

ity, stability, testability, privacy of the models, ubiquity

Internal quality: accuracy, suitability, interoperability, robust-

ness, self-contained, independence

Quality of use: understandability, learnability, operability, at-

tractiveness, appropriatednessDynamic qualities within a selected development approach

executability, refinement quality, scope restriction, effect preserva-

tion, context explicity, completion tracking

modelling properties: monotonicity, incrementality, ... see below

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Success Story: Model Suites for HeartModelling�� Multi-scale modelling

5-layer model of the heart

Genes layer: networks, based on molecular functions

Proteins: elementary units, chemistry, and their composition

Cell structure: functions, key organisational unit; with biological

processes; pathway models

Tissue: structure and function, with cellular components

Body: myocardian activation

time range: 1015

space range: 109

c⃝Peter Hunter, see too: International Institute for Theoretical Cardiology

http://www.bioeng.auckland.ac.nz/People/people display.php?people id=353

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Success Story: Model Suites for HeartModelling�� Multi-scale modelling: 5-layer model of the heart

Tissue: structure and function, with cellular componentsbased on mathematical models for structures and geometry

• mechanics,• kinematics,• equations (equilibrium, constitutive),• conditions (e.g. boundary),• factors (e.g. stress)

myocardian activation

coupled with electro-mechanics, e.g. energy flow

modelling cell processes• electro-physical• proton and biocarbonat• calcium• myofilament


30 years of heart modelling conference July 2009

http://www.physoc.org/custom2/publications/proceedings/archive/article.asp?ID=J%20Physiol%20561PSA12

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Success Story: Model Suites for HeartModelling�� Multi-scale modelling: 5-layer model of the heart

• Biophysics of nerve and muscle: cable theory, ionic currents, Hodgkin-Huxley

equations, muscle models (anatomy, contraction, sliding filament theory, ener-

getics), fading memory model (finite duration length step, force step response)

• Cardiac electrophysiology: cardiac cells, units, diFrancesco-Noble model, mem-

brane models, bidomain model• Electrocardiography: cardiac anatomy and function, activation, body surface

potential mapping, transfer matrices, myocardial inverse procedure, normal and

abnormal ECG �� Example cell.ml and field.ml

• ontologies, content models• molecular function, biological process, cellular component


the virtual heart (2004 breakthrough) http://www.wellcome.ac.uk/News/2004/Features/WTX023668.htm

modelling the human heart http://www.uniservices.co.nz/pageloader.aspx?page=741d8d0d82

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Lesson Learned: Model Suites�� Handling abstraction

Structure and association based on• multi-layered models• constructors and languages

Topology and geometry based on• topological space• (homo-)morphisms and equivalence

Algebra and evolution based on equations, development rules,

and restrictions• mass balance• charge balance• osmotic balance• thermodynamic balance (e.g. feasibility)

Abstraction and refinement based on• mappings• informorphisms

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Our Approach to a Science of Modelspurpose: using for construction, simulation, theory development,

prognostics, documentation, ...

context: language, background science or postulates, culture, as-

sumptions, special conditioning, ...

stakeholder: author, developer, implementer, documenter, user, ...

model - wherefor - whereof - wherewith - how -wherein - where - for what - wherefrom

additionally: whereat - whereabout - whither - when -whence - by whom - to whom - whichever - what in - what outwhereto - for which reason - why

Thus our program: (1) language; (2) image of origin(al);

(3) purpose; (4) value

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Fundamental Tenets of Modelling Science

Mental world of a person is based on

• structures both implicit (e.g., background) and explicit and

• processes both implicit (e.g., background) and explicit.

Conceptual structures include conceptions (concepts, the-

oretical statements (axioms, laws, theorems, defintions), models,

theories, and tools). Conceptual processes include procedures and

associated norms and rules.

Both are based on paradigms (theories, science, assumptions, condi-

tioning!!) which are corroborated.

Models support interaction, understanding, sharing, and collaboration

among people! They depend on existing knowledge, the actual (onto-

logical) state of the reality, the condition of the person’s senses and

state of mind, and the state of employed instruments.

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Knowledge? Define????Different kinds of to know

(1) The state or fact of knowing.

(2) Familiarity, awareness, or understanding gained through experience

or study.

(3) The sum or range of what has been perceived, discovered, or

learned.

(4) Learning; erudition: teachers of great knowledge.

(5) Specific information about something.

(6) Carnal knowledge.

compare idioms to German meaning

come to one’s knowledge etw. ist zu jmdm. zu Ohren gekommen

safe in the knowledge im beruhigenden Wissen

to one’s knowledge so weit man weiß

to the best of one’s knowledge nach bestem Wissen

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Knowledge is backing ModelsKnowledge as sustainable, evolving, potentially durable and verifi-

able grounded consensus: The required data chunk can be qualified as knowledge,

if it(1) is consensus within a world and a community,(2) is based on postulates or principles that create the fundament for the knowledge,(3) is true according to a certain notion of ’truth’,(4) it is potentially evolving within an ordered evolution/aging process,(5) is reusable in a rule system for new information,(6) is has a longer lifespan and exists with persistent validness,(7) has an effect and is sustaining within a society, community or world, and(8) is not equivalent to other information that can be generated with the aid of facts or

preliminary information in the particular inventory of knowledge by a rule system.

Knowledge as the state of information of a user:Different kinds of ‘to know’ are for a human:(1) The state or fact of knowing.(2) Familiarity, awareness, or understanding gained through experience or study.(3) The sum or range of what has been perceived, discovered or learned.(4) Learning; erudition: teachers of great knowledge.(5) Specific information about something.(6) Carnal knowledge.

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Paradigmis a conceptual system that governs explicitly a person’s consciousexperience in a given situation

(1) determines the conditions that trigger every voluntary activity in the experience

(2) sets standards, rules, and guidelines for choosing and processing

(3) provides necessary conceptions, conceptual tools and methodology

(4) supplies appropriate mnemonics

Every human experience is paradigm-laden. (Kuhn)

Scientific paradigm: what members of a scientific community share, and conversely,

a scientific community consists of people who share a paradigm.

(1) ontological tenets about (physical) realities

(2) scientific theory or set of theories about realities

(3) specific restricting methodology (standards, tools, rules, guidelines, processes)

(4) axiological tenets

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Myths of Modelling

(1) Modelling equals documentation

(2) You can think everything through from the start

(3) Modelling implies a heavyweight software process

(4) You must “freeze” requirements

(5) Your design is carved in stone

(6) You must use a CASE tool

(7) Modelling is a waste of time

(8) The world revolves around data modelling

(9) All developers know how to model

(10) Modelling is independent on the language�� Let’s discuss the last point in detail

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Wrong or Inappropriate Language: TrafficControl based on Petri Nets

Simple but classical solution

Picture becomes more complex if time control is incorporated

N/S green

:

N/S red

z

W/E green

W/E red

y

Time for switch

s�

Time for wait

9j

�

:

Y

j

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Wrong or Inappropriate Language: Look atReality

• Situation in reality

• Neighbourhood street crossing

must be considered

• Complex time management

• Different kinds of traffic:

pedestrians, tram, bus

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Wrong or Inappropriate Language: TrafficControl based on Petri Nets

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Wrong or Inappropriate Language: TrafficControl based on State Charts instead on

Petri Nets

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Local-as-View Modelling: Traffic Controlbased on ASM and Conceptual Modelling

Global states: NSredEWgreen, NSredEWred, NSgreenEWred

optionally: NSredEWred, NSnothingEWnothing

avoiding conflict states such as NSgreenEWgreen

Abstract state machine rules:

Controller location state clock reset switch

e ... ... ... ... ...

if Switch(e) then Update(e,collocated(e)); ChangeAction(e)

ChangeAction := getState; choosePossibleStateChange(state);

apply(possibleStateChange(state))

AlarmAction := on alarm changeStateToErrorState

Clock := on tick observeWhetherChangeRequired

NormalAction := if change = true then ChangeAction

PedestrianCall := on callAtPoint(cp)

ChangeNextStepIssuedAt(cp)

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Local-as-view Modelling: Traffic Controlbased on ASM and Conceptual Modelling

Global states: NSredEWgreen, NSredEWred, NSgreenEWred

Abstract state machine rules

Database as the backend machine

StateKind

GlobalLightState

� -KindOfState

-StateChange

��

6

RequestChange

StateChangeKind

RequestPedestrianCallPoint

⊕ -�TimerScheduleStrategy

NextStepIssuedAt

local-as-view: π∗EW (GlobalLightState) for EW direction

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The Cognitive Insufficiency of theEntity-Relationship Modelling Language

• The container schema define the distinction between in and out. They have an interior, a

boundary and an exterior.

• The part-whole schema define an internal structuring and uses whole, part and configuration

as construction units.

• The link schema connects thing of interest. It uses various kinds of links for associating or

un-associating things.

• The center-periphery schema is based on some notion of a center. Peripherical elements

are not as important than those in the center.

• The source-path-goal schema uses source (or starting point), destination, path, and direc-

tion. It allows also to discuss main and side tracks.

• Typical ordering schemata are the up-down, front-back and the linear ordering schema.

They use spatial and temporal associations.

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The Lesson: Sapir-WhorfB.L. Whorf, Lost generation theories of mind, language, and religion.

Ann Arbor, Mich., Popular Culture Association,

University Microfilms International, 1980.

D. Sapir, General causation, Synthese, 1991, 86, 3, 321–347

“Principle of linguistic relativity”: actors skilled in a language

may not have a (deep) understanding of some concepts of other

languages

The design and development quality depends on main success factors:

• structuring of the process itself,

• culture of people involved,

• skills of actors, and

• process capabilities

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Main Guiding Concerns of ConceptualModelling

(1) Modelling language constructs are applied during conceptual mod-

elling. Their syntactics, semantics and pragmatics must be well

understood.

�� 30% coverage in most modelling methodologies because of “iron” selection

(2) Application domain gathering allows to understand the problems

to be solved, the opportunities of solutions for a system, and the

requirements and architecture that might be prescribed for the so-

lution that has been chosen.�� 60 % coverage in most modelling methodologies because of pressure, ...

(3) Engineering is oriented towards encapsulation of experiences with

design problems pared down to a manageable scale.�� 10 % coverage in most modelling methodologies because ????

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Application Domain Concern��

��Everybody develops his/her schema, his/her implementation

Application domain development based on “fly by”

• mostly by domain workers without IS insight

• scope of current (and not future) interest

• point of view of subject (subject-oriented)

• tolerance level of users

Flat schemata based on

• binarisation,

• without views, and

• without internal computational support

Near-sighted reflection of ongoing demand

• data needed for analysis but not at the right granularity, precision

• data cleansing is of less importance

• “my-data-is-my-personal choice”

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The Neglected Concern: Engineering��

��ingenerare, “to create” and/or “to contrive”

Sufficient quality instead of optimal quality defined on the

purpose

e.g. integrity constraints that are really necessary

Living with errors as long as users can live with them, living with

enforcement and fetching modifications in time

Living with deficiencies of technology, e.g. missing support for

full storage (sliding window techniques)

Providing forgetful data handling with automatic background

archiving or deletion or distribution

User-demand driven query answering, search drill-down, ea-

ger/lazy enforcement, data granularity, variety of views depending

on task/profile/role under consideration

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Language Layers of Specification�� Specification Engineering: Separation and Abstraction

(1) Declaration layer based on logical formulas or constructs description

(2) Technical layer, e.g., methods for maintenance, by rules for compensation,

enactment strategies, auxiliary methods

operational semantics

(3) Technological layer under explicit consideration of implementation and re-

finement context

application

(4) Organizational layer by integration into the architecture of the system, by

obligations for users and for components of the system

establishment

(5) Economical layer: (economical and technological) feasibility, quality satis-

faction

(6) Handling satisfaction of properties and predicting changes of satisfac-

tion

(7) Optimisation for evolution and adaptation

(8) Experiences utilisation for innovation and generalisation

41


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Introduction

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Concerns

Model

Modelling Act

Principles

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Concept Topic

Content

Information

c⃝B. Thalheim

The Notion of the Model�� Different flavours of the notion of a model(1) A system of postulates, data, and inferences presented as a mathematical description of an entity or state of affairs.

Encyclopedia Britannica

(2) Graphical, mathematical (symbolic), physical, or verbal representation or simplified version of a concept, phenomenon,

relationship, structure, system, or an aspect of the real world. The objectives of a model include

(1) to facilitate understanding by eliminating unnecessary components,

(2) to aid in decision making by simulating ’what if’ scenarios,

(3) to explain, control , and predict events on the basis of past observations.

Since most objects and phenomenon are very complicated (have numerous parts ) and much too complex (parts have

dense interconnections) to be comprehended in their entirety, a model contains only those features that are of primary

importance to the model maker’s purpose. Models range from simple sketches to computer programs with millions

of lines of code, but all of them have one thing in common: some elements of the actual ’thing’ are abstracted or

mapped into the model. Models are divided into three classes on the basis of their degree of abstraction.• (1) Iconic model: least abstract , physical, ’look-alike’ model, such as a model airplane or train .• (2) Analogous model: more abstract but having some resemblance to what it represents, such as a chart, graph,

map, network diagram .• (3) Symbolic model: most-abstract model with no resemblance but only an approximation to what it represents,

such as a mathematical equation or formulaBusinessDictionary.com

(3) A schematic description of a system, theory, or phenomenon that accounts for its known or inferred properties and

may be used for further study of its characteristics: a model of generative grammar; a model of an atom; an economic

model.Collins English Dictionary

(4) A systematic description of an object or phenomenon that shares importantcharacteristics with the object or phenomenon. Scientific models can be material, visual,

mathematical, or computational and are often used in the construction of scientific theories.Mariam-Webster Nr. 12

42


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ModelThe model is an artifact that is considered to be worth for its purpose

by the author.

Model dimensions:

(a) main dimensions are

purpose dimension (“wherefore”) clarifying the mission of the model

artifact dimension (“whereof”) as a result of the mapping,

carrier dimension (“wherewith”) ,e.g., language used for representa-

tion of the model within its capability and limitations,

value dimension (“worthiness”)

(b) context dimensions are

stakeholder dimension governs the viewpoint, orientation and back-

ground of users involved

application domain dimension scope and (explicit and implicit) disre-

gard of the model

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The Model· is based on an analogy of structuring, functionality, or behaviour,· considers certain application purposes, and· provides a simple handling or service or consideration of the thingsunder consideration.

Model capacity:

· the model provides some understanding of the original;

· the model provides an explanation of demonstration through auxiliaryinformation and thus makes original subject easier or better to understand;

· the model provides an indication and facilities for making properties viewable;

· the model allows to provide variations and support optimisation;

· the model support verification of hypotheses within a limited scope;

· the model supports construction of technical artifacts;

· the model supports control of things in reality;

· the model allows a replacement of things of reality and actsas a mediating means.�� Choose whatever you like and master!?!?!?

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Goal, Purpose, Function of a Model

state(s)(of affairs)

�Y

targetstate(s)

currentstate(s)

�

�

goal

?communityof practice

� purpose

?means

� function

?application‘game’

goaldimension

purpose dimension

deployment function dimension

A model is simply

• a material or virtual artifact

• which is called model within a community of practice

• based on a judgement of appropriateness for representation of other artifacts

(things in reality, systems, ...) and

• serving a purpose within this community.

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Explicit Treatment of Model Intentions�� Models have their goals, aims and intends that should be separated

Perception support for understanding the application domain

Explanation and demonstration for understanding

Preparation to management and handling of the original

Optimisation of the application domain operating

Hypothesis verification through the model

Construction of an artifact

Control of parts of the application

Substitution for a part of the application�� Depending of the aim we shall use different schemata!

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Construction Principles�� Constructors for main structures and escort structures

star snowflake

special

implementation

meta

log ...

history

strategic tactical

layers

utilisation

security performance

quality

processing context utilisation

context

based on

bulk

potential actual

based on

product ... multiset

constructor component hinge

associations

classification

type semantics

hierarchies

constructors

principles of constructing complex structures

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Explicit Consideration of ModelPurpose/End

Construction purpose for construction of a solution to application

domain problems (either as business system or as embedded system)

Communication purpose among stakeholders

Analysis purpose for validation, verification, tests

Examination ad check purpose for application domain or con-

structed system

Documentation purpose for logging development decisions, alter-

natives, neglected parts, variants, reference models

Master complexity, improvement, evolution, and realisation

�� Each purpose requires its constructions and approaches!

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Ex.: IS Models in GeneralI. Analysis: Says what is.

The model does not extend beyond analysis and description. No causal relationships

among phenomena are specified and no predictions are made.

II. Explanation: Says what is, how, why, when, and where.

The model provides explanations but does not aim to predict with any precision. There

are no testable propositions.

III. Prediction: Says what is and what will be.

The model provides predictions and has testable propositions but does not have well-

developed justificatory causal explanations.

IV. Explanation and prediction (EP): Says what is, how, why, when,

where, and what will be.

Provides predictions and has both testable propositions and causal explanations.

V. Design and action: Says how to do something.

The model gives explicit prescriptions (e.g., methods, techniques, principles of form

and function) for constructing an artifact.

c⃝ Gregor’s Taxonomy of Theory Types in Information Systems Development Research

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Main Purpose for IS: Description used forPrescription

Construction workflows are based on creation of models (as images, representations

or portraits of the origin) that are used for production of systems (using as models

as groundwork, background, pattern, standards, prototypes for the system). This

kind of model exploitation uses the dichotomy of models as image of an origin

and groundwork for a system.

• goal of a model: binary relation between final (and initial) states and stake-

holders

aim, ambition, destination, end, intent, intention, object, objective, prompt, target

• purpose of a model: ternary relation between states, stakeholders, and

means (instruments)

adding to goal: instruments, intended purpose, object, purpose (of), scope

• function of a model: quadruply relation between states, stakeholders,

means, and practice

Wittgenstein: “Gebrauchsspiel” how, when, for which/what or why, at

what/which (business use case)

adding to purpose: application, conventions, custom, exertion, habit, handling, deployment, service, usage, use, using

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Central Property: Invariance of Purpose�� Deep Understanding of the Purpose

application origin model 1 model 2

application do-

main with problem

city Konigsberg

with bridges

rough topographical model with

nodes (area) and edges (bridges)

graph with degree

of incidence

application do-

main with problem

city Konigsberg

with bridges

rough topographical model with

nodes (area) and edges (bridges)

QPPPPPPR tree scan-

ning /

c⃝B. Mahr

��

��Model 2A: an Euler path exists iff card({n | degree(n) is odd}) ≤ 2

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Konigsberg Bridge Problem: Model 2B

B... C... D...

A B≈C

B/

D

C

C/ B/

D

A

D/

C B/

A

D/

B/

A

C

D

B

A/ D/

B

A

B/

A D/

B

D

C

B/ C/

A

B

A/

B

A

C

D

A

C/

D/

B/ C/

A

B

A

B/

B/ D/

A

C D/

A

B

D

C

A... B... C...

D

all

��

��And what about the general case? Write a program and verify!!!

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Lesson Learned for Models: Viability�� How Well-formed and Optimal-Suited ?

(1) validity for purpose

(2) reliability and degree of precision

(3) efficiency for purpose satisfaction

(4) extent of coverage depending on purpose

Main characteristics for viability

Empirical corroboration according to purpose, background

Rational coherence and conformity

Falsifiability with tests, reduction, parsimony

Stability and plasticity scope, frame background

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General Frame for Models�� Frame for construction and deployment of models

founding concepts: base conceptions/concepts (scope, expressions, concept

space organisation, quantification/measurement), namespace/ontology/carrier, defi-

nitions (state, intrinsic, object, interaction descriptors and depictors), cargo

structure and function: incremental?, facets (topology/geometry, state, in-

teraction, causal), correspondence (analogy, ...)

application context: application domain, empirical scope of the model, corre-

spondence, laws

meta-model: basement, paradigms and theories; status in the application; context;

proneness for paradigmatic evolution (within the epistemological profile of stakehold-

ers); abstraction level, scale

so far only: intuitive, hidden/implicit (1., 3., 4.)

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Prescription by Models and Description forModels

worlds oforigins

worlds ofartifacts

considered to bemodels(1) mapping

purpose: description of the world

-

(3) pragmatic property

(2) truncation property (4) amplification property

(5) distortion property

(6) idealisation property

observations

descriptions

experimentslanguage

assessment of model value in dependence on purpose

derivation of properties of interest in dependence on purpose

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Prescription by Models and Description forModels

worlds ofrealisations of

models

world ofartifacts used as

models (6) calibration

purpose: prescription of constructions

-

(4) amplification property

(5) distortion property

(7) properties

observations

descriptions

experiments

hypotheses

learning by/with the model

prediction with the model

language

mapping

evaluation of the value of the model depending on purpose

application for theory development and hypothesizing

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The Origin-Model-Author-UserRelationship

addressee

modelorigin

author

grasps uses

intention

develops

analogy

invariance of essential properties

abstraction from irrelevant properties

extension by useful concepts

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Languages as the Background forModelling

Ψ(G)postulatesobjectives

-corresponds

LGlanguage

Gsubjectartifact

Φ(G)statements

⊇

scope-

Formal languages are based on

• postulates that restrict the applicability and utility for modelling

• structures (signature) and entities which are used for interpretation

• restrictions to words that might be considered

We thus must consider the Sapir-Whorf principle of relative relativity.

We do not request that all entities are described in the same fashion.

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Languages for Artifacts

Ψ(G)postulatesobjectives

-corresponds

LGlanguage

Gsubjectartifact

Φ(G)statements

⊇

scope-

Examples:

LG Ψ(G) corresponds G scope Φ(G)

logics axioms satisfy structure satisfy essential

properties

N Peano ax-

ioms

satisfy standard

model

derivable Peano arith-

metics

empirics postulates accepted origin permits observation

technics construction

requirements

enforced product has properties

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Modelling as a Mapping Problem

O:Original suiteused for model

Φ(O)propertiesof objects

with relevance for user

⊇

abstraction

Theor(O)theories about objects

LOapplication domain language

for objects in the origin

-Ψ(G)model

objectivesunderstoodby the user

--understand

map

integrate G:model

selected by the user

Φ(G)properties

of the modelused for model purpose

⊇

abstraction

Theor(G)model theories

LGlanguage for declaration of artifacts

-

Central property: purpose invariance for the mapping from origins

to images

• realising the analogy property

• back transformation from images to origins for statements of the

origin

• extended utilisation of (model) artifacts for other purposes Weiter-

verwendung des Gegenstandes fur andere Zwecke

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The Modelling Act�� similar to the speech act

(1) a selection and construction of an appropriate model depending

on the task and purpose and depending on the properties we are

targeting and the context of the intended system and thus of the

language appropriate for the system,

(2) a workmanship on the model for detection of additional information

about the original and of improved model,

(3) an analogy conclusion or other derivations on the model and its

relationship to the real world, and

(4) a preparation of the model for its use in systems, to future evolution

and to change.

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Modelling as an Activitywithin (at least) two facets:

model deployment based on activities

• adaption, concept enrichment, optimisation, specialisation, instantiation,

refinement, grinding

• applicability studies (evaluation, assurance, composition for application)

• integration, selection

• problem solution, classification, practice, understanding, theory or paradigm

(r)evolution

• explanation

model development based on activities

• abstraction of origin, scoping, validation, verification, testing, optimisation

• construction, composition, definition, integration, classification, invention

• enrichment, adaption, mutation, recombination, refinement, side reuse,

preparation for deployment

• understanding, theory or paradigm injection

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Modelling Workflows

relevance stage modelling stage realisation stage

world of applications world of models world of systems

derivation ofapplication domaingoals and objectives

?elicitation of

application domainentities

?

reexamination ofentities and properties

�

�derivation of

application domainproperties

-

?

derivation ofmodelling objectives

?developmentof the model

selectionof quality criteria

-

?derivationof modelproperties

6?

-

evaluation andanalysis of the model

�

�

derivationof implementation

objectives

?development of theimplementation

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Modelling Workflows: Description-Prescription-Specification-Coding

O

RΦ(O)

reasoning about ways of operatingin the application domain

reasoning about propertiesthat are of relevance for the problem

RΨ(M)

reasoning about objectiveswhich must be satisfied by the model

RM

reasoning about the model, its architecture,its composition, its elements

RΦ(M)

reasoning about properties of the modeland satisfaction of model objectives

RΨ(Y )

reasoning that targetson realisation objectives

RY

coding-orientedreasoning

RΦ(S) assessment and

guarantee reasoning

reasoningonrelevance

relevancestage

reasoningonmodelling

acts

modelling

stagereasoning

onsystem

engineering

realisationstage

prescriptionspecification

description

coding

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Modelling Workflows: SystemConstruction

(3) derivation ofrelevant entities

in application domain

-

(2) derivation ofdevelopment goals inapplication domain

?(4) derivation of

application domainproperties

�(1) settlement ofdevelopment

purpose

6

application domain sub-workflow

⇒

(3) modelling activities,derivation of

relevant conceptsto be used in model

-

(2) mapping ofapplication domain

properties

?(4) derivation of

modelproperties

�(1) calibration tomodelpurpose

6

sub-workflow during modelling activities

⇒

(3) ‘normalisation’,change

obligations

-

(2) crudequalityanalysis

?(4) changemanagement

�(1) mapping ofquality

characteristics

6

model evaluation sub-workflow

⇒⇒⇒

(2) implementationmanagement

-

(1) mappingmodel properties to

implementation objectives

?

(3) component development,coding,

component integration

�

(4) qualitymanagement,

testing, verification

6

implementation sub-workflow

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Modelling Workflows: Conceptualisation

--- (1) conceptrequest

- (2) requestdifferentiation

- (3) understand& explain

?

(4) evaluate& select

?(5) conceptdescription

�(6) conceptjustification

�(7) experiment,test,

evaluate

�

�

(8) applyconcept

��

conceptualisation cycle

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General Principles�� Well-known principles but missing theory

Modularisation: based on relatively independent component or sub-

ject architectures, orthogonalisation, parametrisation, based on

Parnas information hiding and Liskov substitutability principles

with interfaces, obligations, views, hierarchies, responsibilities, inte-

gration, dependences, independence, incompleteness and fuzziness,

open world potential composability

data, functional and control decomposition

skeleton or crystallisation techniques

Abstraction: component or construction abstraction, context ab-

straction, implementation abstraction

pattern techniques and refinement, meta-structures, views

Coupling: easy/lazy enforcement, tight or loose coupling, integration

or collaboration

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Principles�� Esko Marjomaa: “Peircean” Reorganization in Conceptual Modeling Terminology

Conceptualization principle: Only conceptual aspects of the application domain

should be taken into account when constructing the conceptual schema.

95% -principle: All the relevant aspects of the application domain should be de-

scribed in the conceptual schema. instead of 100% principle; based on engineering insight

Formalization principle: Conceptual schemata should be formalisable in order to

be implementable.

Semiotic principle: Conceptual schemata should be easily interpretable and un-

derstandable.

Correspondence condition for knowledge representation: The modellens

should be such that the recognizable constituents of it have a one-to-one cor-

respondence to the relevant constituents of the modellum.

Invariance principle: Conceptual schema should be constructed on the basis of

such entities found in the application domain that are invariant during certain

time periods within the application area.

Sub-schemata principle: In order to construct a good conceptual schema it is im-

portant first to construct relevant sub-schemata and then to search for connections

between them.

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Models and Solutions Imperfectionexplicit modelling of the divergence from the real world

• incompleteness

• delay

• simple error

any model is imperfect due to

exceptional states events, time lags

incompleteness to to limitations of the language, consideration,

errors either based on real errors and exceptional states or based on

biases

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Properties of the Analogy Association:Adequacy

likeness of origin and model depending on purpose and deployment

with explicit likeness relationship

regularity for deployment with exact rules scope within a (well-

founded) system

with reasoning support based on some kind of formal system

fertility due to the potential for generating insight of high utility

capacity of the model

simplicity of the model based on the truncation property, on abstrac-

tion, concentration to the essential and relevant elements

thus better explanation, simpler solutions, easy relaisation, ...

plausible reasoning on the basis of abduction, induction

analog / autoepistemic / default / defeasible /non-monotonic ...

reasoning, also reasoning by (counter-)examples

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Properties of Modelling

Monotonicity: any change leads to a refinement

Incrementality: any step is only based on new requirements or obli-

gations and on the current specification

Finiteness: any quality criteria can be checked in finite time applying

a finite number of checks

Application domain consistency: corresponds to the require-

ments and the obligations of the application domain

Conservativeness: any model revision that cannot be reflected al-

ready in the current specification is entirely based on changes in

the requirements.

at least conservative and application domain consistent

any finite modelling process can be transformed into a process

that is application domain consistent

if the modelling process is application domain consistent then it can be

transformed into an incremental one if we can extract such area of change

in which consistency must be enforced

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Reasoning on Properties

Subject of modelling depending on the application area (DSL), or-

ganisation units, scope, selected language, language level and ex-

pression style, categorisation, classification, terminology

Quality of the model depending on the modelling language, mod-

elling transformations, formal treatment, formal derivation of prop-

erties, measurement

reason whether a model is appropriate or suitable, relative com-

pleteness (Frege) depending on the purpose of the model

depending on the quality of activities, goals, relevance, reusability,

maturity, understandability

Economy of modelling partially using standards, evaluating cogni-

tive distance, evaluation value of the model, ROI

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Quality of a Model and Modelling

Previously unknown

UnexpectedNot deductable

Novelty

Parsimony

ClarityCoherence

Simplicity

Comprehensibility

RealisabilityRelevance

Power for reasoning

Usefulness

Range of validity

Conditions of validityGenerality

Provability

TestabilityValidatableProbable

Correctness

Assessment

parsimony: the quality of being careful with resources

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Scienceis any system of knowledge that is concerned with the physical world and its

phenomena and that entails unbiased observations and systematic experimentation.

In general, a science involves a pursuit of knowledge covering general truths or the

operations of fundamental laws.Encyclopedia Britannica Science is treated in a number of articles. For the history of Western and Eastern science,

see science, history of. For the conceptualization of science and its interrelationships with culture, see science, philosophy of. For

the basic aspects of the scientific approach, see physical science, principles of. For the historical development of the different

sciences and their scope, component disciplines, methods, and principal problems, see physical science; Earth sciences; biology;

medicine, history of; engineering; social science.

1 the state of knowing : knowledge as distinguished from ignorance or misunderstanding

2a a department of systematized knowledge as an object of study

2b something (as a sport or technique) that may be studied or learned like systematized knowledge

have it down to a science

3a knowledge or a system of knowledge covering general truths or the operation of general laws espe-

cially as obtained and tested through scientific method

3b such knowledge or such a system of knowledge concerned with the physical world and its phenomena

: natural science

4 a system or method reconciling practical ends with scientific laws

5 ...

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Theory(1) belief, policy, or procedure proposed or followed as the basis of action

Synonyms: hypothesis, supposal; compare assumption

Related word: base, basis, grounds, position, premise, understanding

Antonyms: practice(2) something taken for granted especially on trivial or inadequate grounds

Synonyms: conjecture, perhaps, speculation, suppose, supposition

Related word: guess, guesswork, surmise; feeling, hunch, impression, presentiment, suspicion

Contrasted words: assurance, certainty, knowledge

Encyclopedia Britannica

1 the analysis of a set of facts in their relation to one another

2 abstract thought : speculation

3 the general or abstract principles of a body of fact, a science, or an art

4a a belief, policy, or procedure proposed or followed as the basis of action

4b an ideal or hypothetical set of facts, principles, or circumstances

5 a plausible or scientifically acceptable general principle or body of principles offered to explain

phenomena

6 a a hypothesis assumed for the sake of argument or investigation

6b an unproved assumption : conjecture

6c a body of theorems presenting a concise systematic view of a subject

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The Principal Element Types of Modellingand SPICE

Document*

Step*

Layer

Aspect

*

Modelling Input Product

Output WorkProduct

Base Practice

*

Process*

*

*

*

Process Group

*

SPICE Process Dimension

Generic Resource

Generic WorkProduct

Generic Practice

*

Process Attribute*

*

*

*

Capability Level

*

SPICE Capability Dimension

-

-

-

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The Strategic, Tactical and Support Layersof Modelling

Strategic layerPurpose

ofmodelling

Empiricaltreatment while

modelling

Paradigmsbackground for

modelling

Generalprinciples formodelling

...

Tactical layer

Modellingconstructs

Modellingactivities

Modellingproperties

-�

�

I

R

Modelling in the narrow sense

Modellingactor

context

Modellingresources

Applicationdomaincontext

Engineeringapproach

Theorycontext

Referencemodelcontext

Platformcontext

Stakeholder/partnercontext

Support layer

Toolsupport

Mappingfacilities

Facilities forcollaboratingdevelopment

...Reasoning, vali-dation, verifi-cation, testing

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Summarising: The Gaps of ConceptualModelling

Modality

“Partial reality”

Exactness Confidence

6

?

Usage oftheory

Foundation ofdecisions

Modeler

6

?

actswithin

Context

6

Modellingdecision

� URevisionduring the

development process

6 6

Things ofreality Predicator

Partof reality

“Topic”�

6

-Observedproperty

�

?

underusage

Referencemodel

“Schema” as resultand partial point of view

of a databasedevelopment process

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Combining CM and Design Science

describing,elicitating

properties, criteria

application domain,observable phenomena

and actual/goal relations

prescribing,objective gathering,

explaining,representing, imaging

developed modelfor perception andunderstanding

conceptualising,concept gathering,ontologisation,

grounding, tagging

local concepts,concept world

within audience context

-agile

perform

creating & constructing

of systems,

creating support,

integrating into

infrastructure & context

artifacts andsystem components

based on means/end relations

6

observe

6

design

6

understand

-prepare

for control

-integrate

knowledge

R

defined

perform

?establish

?predict

deploying,documenting, guiding,

mastering

behaviour description,guidance,prompter

R

perform

with sense

culturing of design,developing methods,

founding by paradigms,generalising

meta-artifacts, pattern,scientific foundation,experience, expertise

state of affairs,

phenomena,

requirements and demands

for systems support

reality

augmented reality

relevancecycledesign/m

odellingcycle

rigorcycle

6

knowledge-perception-comprehension

?

backgrounding-mediating-coding

state-of-affairs

state-of-augmentation

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Programme and Problems Discussedthroughout this Talk

(1) Is there any general theory of conceptual models?Yes, we can!

(2) Restricting consideration to language-based modelsSystematic treatment based on language mappings.

(3) Models as artifact, image and prescriptionWorkflows and stories of modelling.

(4) Purpose orientation of modelsThe purpose drives modelling, carries, analogical reasoning, and added

value.

(5) Semiotics as a language backgroundWell-defined syntax, semantics and pragmatics.

(6) Principles and theoryTheory, art and science of modelling.

(7) Modelling acts and modelling workflowsSystematic and well-defined support.

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Summarising�� ... although there is not too much to summarise�� Instead of my own summary: The consternation summary at Modellierung 2009(1) ... but they do not know what they do ...

(2) Babylonian language confusion and muddle

(3) “It’s not a bug, it’s a feature”, de-facto-standards and lobbyists

(4) why I should cope with what was the state of art yesterday

(5) each day a new wheel, new buzzwords without any sense, and a new trend

(6) without consideration of the value of the model

(7) competition is a feature, inhomogeneity

(8) Laokoon forever

(9) dreams about a sound mathematical foundation

(10) take but don’t think - take it only without critics

(11) academia in the ivory tower without executable models

(12) where is the Ariadne thread through�� Our goal: Overcome this situation!

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Some Papers�� Conceptual modelling

D. Embley, B. Thalheim. The Handbook of Conceptual

Modeling: Its Usage and Its Challenges. Springer 2011

Modelling is the programming in the 21st century!!�� B. Thalheim: Minimal reading

• Towards a Theory of Conceptual Modelling. Journal of Universal Computer

Science, 2010, 16, 20

Preliminary version: Lecture Notes in Computer Science 5833

• The Art of Conceptual Modelling, Proc. EJC’2011

• The Theory of Conceptual Models, the Theory of Conceptual Modelling and

Foundations of Conceptual Modelling. Handbook of conceptual modelling.

• The Science of Conceptual Modelling. DEXA’2011, LNCS 6860

• Syntax, Semantics and Pragmatics of Conceptual Modelling. NLDB’2012,

LNCS (forthcoming)

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Publications on Science and Art ofConceptual Modelling

• A. Dahanayake and B. Thalheim. Towards a framework for emergent modeling. In ER

Workshops, volume 6413 of Lecture Notes in Computer Science, 128–137. Springer, 2010.

• A. Dahanayake and B. Thalheim. Enriching conceptual modelling practices through design

science. In BMMDS/EMMSAD, volume 81 of Lecture Notes in Business Information Processing,

497–510. Springer, 2011.

• B. Thalheim. Towards a theory of conceptual modelling. Journal of Universal Computer Science,

2010, 16, 20, 3102–3137.

• B. Thalheim. The theory of conceptual models, the theory of conceptual modelling and foun-

dations of conceptual modelling. In The Handbook of Conceptual Modeling: Its Usage and Its

Challenges, chapter 12, 543–578. Springer, Berlin, 2011.

• B. Thalheim. The science of conceptual modelling. In Proc. DEXA 2011, volume 6860 of

LNCS, 12–26, Berlin, 2011. Springer. (Journal version in JUCS 2012)

• B. Thalheim. Integrity constraints in (conceptual) database models. In The Evolution of

Conceptual Modeling, volume 6520 of Lecture Notes in Computer Science, 42–67, Berlin, 2011.

Springer.

• B. Thalheim. The art of conceptual modelling. In Proc. EJC 2011, 203–222, Tallinn, 2011.

• B. Thalheim. Culture and art of conceptual modelling. Anwendungsorientierte Organisations-

gestaltung, 127–144. baar, Hamburg, 2011.

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Next: Foundations of ModellingTowards a Science of Modelling

• Semiotics of Modelling

• Model suite

• Knowledge and paradigms

• Two schemata during conceptual modelling

• Models in other sciences

• Concepts

• Semantics

• Pattern

• Languages

• Summarising

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[email protected]

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Content

Information

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The Art of Conceptual Modelling - is.informatik.uni-kiel.de · Conceptual Modelling Art SS 2012 B. Thalheim Introduction Modelling? Problems Concerns Model Modelling Act Principles