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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
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Introduction
Modelling?
Problems
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Principles
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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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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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General Principles and Properties ofModelling��
��(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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General Principles and Properties ofModelling��
��(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
c⃝Peter Hunter, see too: International Institute for Theoretical Cardiology
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
c⃝Peter Hunter, see too: International Institute for Theoretical Cardiology
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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Introduction
Modelling?
Problems
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Model
Modelling Act
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Finally
Concept Topic
Content
Information
c⃝B. Thalheim
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
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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
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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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