The Culture of Conceptual Modelling · The Culture of Conceptual Modelling CAU@Kiel, Vorlesung SS...

The Culture of Conceptual Modelling

CAU@Kiel, Vorlesung SS 2012

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

Part III

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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ConceptualModellingCultureSS 2012

B. Thalheim

Engineering

Components

Techniques

Two schemata

Model

Modelling Act

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Library

Finally

Concept Topic

Content

Information

c⃝B. Thalheim

Diese Folien sind noch nicht abschließend

Es werden weitere Teile ergnzt!!!

These slides are not yet ready and finalized!

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B. Thalheim

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Zwischenstand�� Kann man aus Modellen anderer Wissenschaften lernen?

/ Vielzahl von Anwendungen

/ Modelle werden i.a. immer großer und komplexer

� Modelleinsatz

- riesige Variationsbreite

☼ Modellgranularitat, Abstraktionsgrad

☼ Modelle an sich gibt es nicht

☼ Modellieren immer mit einem Ziel

☼ Modell als Abbild eines Originals

☼ Modelle sind zweckorientiert

☼ Modelle sind sprach- und kontextbezogen

�� Ziel: Allgemeine “Theorie” der Modelle und der Modellierung!

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B. Thalheim

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Modell, Modellieren, Modellierung�� Drei verschiedene Seiten.

Power for reasoning

Theorie der Modellierung

Modellgestaltung

Modellierung

Modell ....Theorie des Modellierens

Modellinstantiierung, -adaptionModellkombination

Modellentwicklung, -bildung

Modellieren

Theorie der Modelle

Top-down- oder bottom-up-Modelle

Sprachen von ModellenEvolution von Modellen

Prinzipien von ModellenFunktionen von Modellen

Modelleinsatz, -nutzung

Modell

Unterscheidung von

Duden:

Modell: (Wiss.) innere Beziehungen und Funktionen von etwas abbildendes bzw. (schema-

tisch) veranschaulichendes und vereinfachendes, idealisierendes) Objekt, Gebilde

modellieren: formen, gestalten, gestaltend bearbeiten, Modell herstellen, bilden

Modellierung: das Modelliertwerden, durch Modellierung geschaffene Gestalt,Form, Beschaffenheit

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B. Thalheim

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Modell, Modellieren, Modellierung�� Drei verschiedene Seiten: Unterschiedlicher Sprachgebrauch!!!

Wortschatz (uni-leipzig.de)

Zeitungs- und Zeitschriftenartikel

aktueller Gebrauch

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B. Thalheim

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Foundations of ModellingTowards a Culture of Modelling

• Engineering

• Components

• Formal Methods

• Techniques

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B. Thalheim

Engineering

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The Neglected Concern: Engineering(Remainder)�

��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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B. Thalheim

Engineering

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Engineering��

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

application of science to the optimum conversionof the resources of nature to the uses of humankind

creative application of “scientific principles to design or develop structures, machines, apparatus,

or manufacturing processes, or works utilizing them singly or in combination; or to

construct or operate the same with full cognizance of their design; or to forecast

their behaviour under specific operating conditions; all as respects an intended function,

economics of operation and safety to life and property”

manufacture or assembly of engines, machine tools, and machine partsengineering: military - civil - mechanical - chemical - industrial

great body of special knowledge

involves extensive training in the application of that knowledge

standards of engineering practice

functions: (scientist; to know verified, systematized knowledge of the physical world),

(engineer; to do and bring knowledge to bear on practical problems)engineer is not free to select the problem that interests him, solves problems as they arise

solution must satisfy conflicting requirements(technical, technological, economical, ..., social)

types of resources: materials, information and energy

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B. Thalheim

Engineering

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Layers of Engineering

(1) Description: constructs, rules, runs, schema declaration

specification

(2) Technical support: methods, rules for handling, strategies and auxiliary

methods

control

operational semantics

(3) Technology integration: explicit consideration of refinements and contexts

(e.g. for update in place, in private or in separation),

transformation to dynamic transition constraints

application

(4) Organizational layer: integration into the architecture of the system, obli-

gations for users and for components of the system

establishment

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

faction

(6) Evolution layer: optimisation, experiences for innovation and adaptation

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B. Thalheim

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Treatment of errors�� be aware and develop mechanisms

Systemic errors e.g. based on data exploration, data re-usage, data merging,

falsifications, biases, wrong models

Systematic errors e.g. due to abstractions, restrictions in accessible data, dirty

data, approximations, computations

Stochastic errors e.g. based on assumptions for occurrence of errors, their dis-

tribution functions and their contribution within the model and to the variables

Handling by

error prevention with direct correction, duplicate extraction and elaboration,

validation, and verification

(probabilistic) error models with maximal error, error spreading and multiplica-

tion, confidence intervals, means, outlier detection, time series abstractions

error cleansing algorithms with(out) data recovery

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Development Dimensions of ConceptualModelling

(0,1)

(0,n)

(0,1)

(0,n)

(0,1)

(0,n)

(0,1)

(0,n)

(0,1)

(0,n)

Resource Work product

Activity

Partner Aspect

performs restricted to

based on uses creates/revises

�� complexity # common misbelief in existence of a theory of CM

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The Work Product DevelopmentDimension�� The main product is the model; it shall however used for realisation!

The work product

depends on abstraction layer e.g. requirements, specification, realisation or

implementation layer,

depends on granularity and precision of the work product itself,

depends on resources used for development such as the languages,

depends on level of separation of concern such as static/dynamic proper-

ties, local/global scope, facets,

depends on quality properties of the input , e.g. requirements, com-

pleteness, conciseness, coherence, understandability,

depends on decomposition of the work products in ensembles of sub-

products, and

satisfies quality characteristics

quality in use

internal quality

external quality

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B. Thalheim

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An Example: Different Kinds of ModelSupport

Finance crisis: background, theories, causes, drivers

Why economy science failed completely? deficiencies, state-of-the-art

Why politicians applied the wrong approach? background, bindings

Is bonus treatment the golden shot? prediction, restrictions

Who is really ruling society and banks? science and reality

Insiders on the crash and lessons learned. learned at all

How the crisis unfolded and how stocks were hit? history beside news

Has the economic recovery really started as yet? analysis

‘The next crisis will make this one look like a warm-up.’ spending tax payers money

How your money was spent on the bail-out? $ 11 trillion bailing out failing banks

Find out how debt has soared in the crisis? cost of the financial meltdown

How Fannie and Freddie sank the US housing market? complete picture

A year of crisis history, state-of-the-art, consequences

see for instance:

http://news.bbc.co.uk/2/hi/in depth/business/2007/creditcrunch/default.stm

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B. Thalheim

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An Example: Resulting Needs

• state-of-the art, -affairs, -knowledge, -science

• deficiencies, missing or withhold facts

• background, scientific explanations, science, potential theories,

analysis

• cross links, bindings

• associations

• facts with quality properties, full or partial picture

• predictions, possible tactics and strategies for the future

• restrictions, generalisation

• analogies

• history beside news

• ways to cope with and the outcome for the future

• consequences

• links with headlines and quality assessment

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B. Thalheim

Engineering

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Information

c⃝B. Thalheim

Model as a Product Depending on theKind of Model

Orientation models in order to zum cope with the situation, to explain, to

survey: history, scenario, facts, summarisation, overall view

Tacit or action model (practices, technics, methods, strategies): rules, proce-

dures, check lists, principles, strategies, law, regulations, comments to regula-

tions

Explanation model with reasons, arguments for explanation of claims or argu-

ments or assertions or recommendations (what, why,, ...)

Sources model (model on information sources (meta model): models on

archives, references to communication, cross links

Editing model depending on the editing or adaptation or processing or ... task:

processing of pattern, operating on analogies, editing for practices, exploration,

for trials and coping with errors

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B. Thalheim

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Mathematical modelsproblem-oriented mathematical

description of the coherence and correlation

of states at input, control and output

of a system or process

with its input values, parameters,

and initial conditions and states

Z, z0, p

Input vectorx

Control flow vectoru

-Output vector

y -

Model formulationM : Φ(X,Z, Y, U, z0, p) ?

Z State vectorz0 Initial state

p Parameter vectorΦ Coherence equation

?Abstraction

Real object suite withmanifold of- elements- properties

- inner and outerefficacious correlation

-Environment Environment

-

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B. Thalheim

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Mathematische Modellierung

Mathematische

ModellierungVersion 11. Januar 2011

Zweck

Lösung realer

Fragestellungen mit

mathematischen

Mitteln (carrier)

Problemdefinition

mit Problemklasse

mit Definitionsrahmennach Polya

nach ß

Lösungsdefinition

mit Definitionsrahmen

mit Rückschlußrahmen

mit Qualitätscharakterisitik

Annahmen

implizite

generelle Gesetze

Dichotomy von Original und Modellim Rückschlußverfahren

durch Original vorgegeben

umfaßt

Funktion,

Aufgaben,

Bestimmung

Funktionen von Modellen

Analyse

KonstruktionEmbedded systems

Business systems

Kommunikationsfunktion

Prüfungsfunktion

Dokumentation

Klassifikation

mathematischer

Modelle

nach

Durchsichtigkeit

Black-box-Modelle

ohne Kenntnisder innerenWirkmechanismen

Bsp. Eutrophierung von Gewässern

hinreichende genaue Reproduktion des Verhaltens

Systemidentifikation anhand von Meßdaten

Training des Modelles

White-box- Modelle mit Kenntnis der inneren Wirkmechanismen

Grey-box-Modelle

mit partieller Kenntnisbzw. mittelbarenParametern

Spielwiese desModellierers

Erklärungsansätze,-konzepte Ökonomie der Nutzung

nach

eingesetzer

Mathematik

statische oder dynamische Modelle Abstraktion vom Zeitaspekt

deterministische versus stochastische durch Zufall bestimmt

kontinuierliche versus diskreteDiskretisierung mit Vergröberung oder Abtastung

kontinuierlich zur Vereinfachung

mikroskopische versus makroskopische

approximative versus exakte Modelle mit Fehlermodell

nach

Problemkreisen

Planungsprobleme

Erklärung phänomenologischer Zusammenhänge

Separationin gute und schlechte Lösungen Optimierungsprobleme

...

Wechselwirkungsmodelle

nach Grad der

Detailisierung

Mikro(skopische) Modelle

Makro(skopische) Modelle

Mikro-Makro-Link der hierarchischen Modell-Suiten

Kriterien

Zulässigkeit

Kriteriumlogisch ohne Widersprüche

auf eindeutige Art formuliert

Richtigkeit

Validierung

an Erfahrungen überprüfen

Kriterium kein Widerspruch zu Tatsachen

Zweckmäßigkeit

Ökonomie keine weiteren überflüssigen Bestandteile

Bewertung eines Modelles

Annahmen

Datenwoher

wie repräsentativ

vernachlässigte Strukturen

Wirkungsmechanismus

Adäquatheit

Ähnlichkeit: Original Modell

Regelhaftigkeit: exakte Gebrauchsregeln

Fruchbarkeit: möglichst viele generelle Aussagen

Einfachheit: so einfach wie möglich

daraus resultierende Einsatzverbote

Modellierungs-

prozeß,

-kreislauf

was ist

die Kunst

der math.

Modellierung

Modellierungs-rezepte

Modellierungs-kreislauf

Bildung: reales Problem -> mathematisches

Analyse, Simulation: mathProbl -> mathLösung

Interpretation: mathLösung -> reale Lösung

Überprüfung: reale Lösung -> reales Problem

andere Modellierungsabläufe

Modellierungsinstrumente

I. Modell-

entwicklung

präzise Bestimmung desrealen Problems

wesentlich

Ziele

Annahmen zur Vereinfachung der Problemstellung

(vereinfachende) Modellannahmen, resultierende Modellgrenzen

Zulässigkeitskriterien, Gütekriterien, Nebenbedingungen

Gesetzmäßigkeitenbestimmende

Substanzwissenschaft

Übertragung aus anderen Modellen

benötigte, überflüssige Informationen

Modellvariablen, -parameter, Entdimensionalisierung

eindeutige Formulierung des mathematischen Problemes

Ableitung der Zielparameter anhand des Modellzweckes mit Bewertung der Umsetzbarkeit

Metaentwicklung

Einfachheit

Bewußtheit des eigenen Vorgehens

explizites Fehlermodell

Randbedingungen, Annahmen, Reduktion der Modelle auf essentielle Elemente, Grenzen

II. Analyse

und Simulation

reales Problem als Leitfaden

analytische Lösungen und qualitatives Verhalten

Spezialfälle, Vereinfachungen, Modellreduktion

Computersimulation und Parameterstudien

III. Interpretation

und Validierung

des Modelles

Redimensionalisierung

Interpretierbarkeit von Ergebnissen

Visualisierung der Ergebnisse

Vergleich mit Beobachtungsdaten und Experimenten

Glättung, Abstraktion von Lösungen

Modellierungs-

pattern

Funktionen

1. Erkenntnis: neue Informationen über das Original

2. Erklärung, Demonstration: z.B. Lernen

3. Indikation: am Modell Eigenschaften sichtbar, meßbar

4. Variation und Optimierung: quantitative Optimierung des Originals

5. Verifikation: vorhandene Konstruktion oder Hypothese überprüft

6. Steuerung: Anleitung zum Handeln, Führungsgröße

7. Projektierung, Konstruktion: zweckmäßige, rationelle, realisierbare Variante

8. Ersatzfunktion: anstelle des Originals

BeispielMathematischeModellierung mitGleichungen etc.

1. Darstellung des realen Problemes (ggf. mit mathematischen Mitteln)

2. Formulierung des mathematischen Modells

3. Analyse des mathematischen Modelles (Vereinfachungen, qualitatives Lösungsverhalten)

4. Lösung des Modells (analytisch, numerisch, ...)

5. Interpretation der Ergebnisse und Vergleich mit dem Ausgangsproblem

6. evt. Verfeinerung des Modells

Separationvon überlagerten Modellen

wesentliche, unwesentliche, treibende, ..., konstante Parameter

Kompositionaus Einzelmodellen

nach Wechselwirkungs-, Integrations-, Austauschmodell

Nebenbedingungen

DatenSchätzungen, Zählungen, Prognose

Granularität, Präzision

Kontext

systematische, systemische, zufällige Fehler

Quellen

vorläufige Version: Abgleich mit speziellen Modellen der Gruppen B, C, D, E nach 2. Runde

ModellierungMathematik.mmap - 03.05.2011 - Bernhard Thalheim

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B. Thalheim

Engineering

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c⃝B. Thalheim

Klassen mathematischer ModelleUnterscheidung nach

• Determiertheit

(deterministische oder stochastische)

• Zeitbezug

(dynamische oder statische)

• Wertetyp

(stetige oder diskrete)

Problemstellung

=⇒problembezogene mathematische Beschreibung

2 Phasen:

• Modellentwicklung (analytische, algorithmische, ...)

• Modellanalyse (je nach Losung analytisch, durch Simulation, ...)

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B. Thalheim

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Verfahrensklassen der Modellauswertung

Analytische Modelle: funktionale Beschreibungen des Originals

durch Gleichungen (algebraische, Differential-, Integral-)

Transformation in algorithmische Modelle oder

analytische Losung (geschlossen oder approximativ)

Bestimmen allgemeingultiger Losungsformeln

Algorithmische Modelle: operationale Beschreibungen von

Prozessen (ablauforientierte Modelle)

erhalten direkt oder aus Transformation analytischer Modelle

(geordnete Dgl., algorithmische Prozeßbeschreibung)

Losung durch Simulationsverfahren

rechnerische (numerische oder numerisch-statische) Nachbildung

des Prozesses (ausgehend von Anfangszustand)

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B. Thalheim

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Modellierung und Simulationhochgradig komplexe Anwendungen erfordern Methoden, mit denen

die vielfaltigen Wechselwirkungen im Zusammenhang erfaßt werden

konnen

mathematische Modelle + computerunterstutzte Analyse

Modelle sind notwendig, da Experimente am Originalsystem

• zu teuer (z.B. Storung des laufenden Betriebs),

• nicht moglich (z.B. humanitare Grunde) oder

• zu langwierig (z.B. langsam ablaufende Prrozesse) sind.

Original-system

Ersatz-system(Modell)

ModelliererExperimentator

ExperimenteValidierung

(Modellierung)

Abbildung-

I �

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B. Thalheim

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Modellierung und Simulation

• Auswahl und Entwicklung eines Modelles

• Manipulierung und Analyse des Modellverhaltens

• Ubertragung der Erkenntnisse auf das Originalsystem

Modelle:

• Physische Modelle

• Biologische Modelle (Tierversuch,...)

• Technische Modelle (Windkanal,...)

• Formale Modelle

• Graphische Modelle (technische Zeichnung,...)

• Mathematische Modelle

• ...

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Beispiel: BedienprozesseWechselwirkung von Serviceanbieter und Servicenutzer

Angebote und Forderungen

Grundstruktur: Warteschlange

• Zwischenankunftszeit

• Bediendauer

• Priorisierung

• maximale Servicezeit

• relativer Beschaftigungsgrad

in verschiedenen Variationen

• Klassenbildendende Merkmale• Ankunftsprozeß• Bedienprozeß• Kanalanzahl• Kapazitat der Forderungen

• Bedienreihenfolge der wartenden Forderungen

• Warteverhalten der Forderungen

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Beispiel: Bedienmodelle

• Stochastische Modelle

Markowsche Ketten, Geburts- und Todesprozesse (mit Intensitat)

Einstufige Poissonsche Bedienprozesse

Geschlossene und offene Wartesysteme

• Jackson-Netze

offenes System aus endlich vielen Wartesystemen

graphische Darstellung

• Gordon-Newell-Netze

geschlossene Bediennetze mit k Knoten

mit lokaler Balance

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B. Thalheim

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Simulation diskreter ProzesseVerfahren zur Analyse und Bewertung von Systemen durch die mod-

ellgestutzte Nachbildung moglicher Prozeßablaufe

Computersimulation mit mathematischen Prozeßmodell

Zustandsvariable meist diskret (digitale diskrete Simulation)

Stochastische Simulation / Monte-Carlo-Simulation mitZufallsvariablen, Zufallszahlen

Ereignisorientierte SimulationNachbildung von Zustandswechseln

• Ereignistyp

• Ereigniszeitpunkt

• Ausfuhrbarkeitskennzeichen

• Prioritat

Unterscheidung in Steuerfluß und Datenfluß

Uberstringen des Verweilens (Zeitraffereffekt)

mit einer Ereignissynchronisation

sowie einer Ereignisplannung

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Algorithmische Modelle als dynamischeModelle

Lastmodell mit temporaren Elementen

Konfigurationsmodell mit permanenten Elementen (Merkmalsvektor)

Zustandsanderungen auf algorithmischer Grundlage

Aktivierungsalgorithmus (Modelluhr, Ereignisliste)

Kausale Modelle von Abhangigkeiten

Entwicklungsmodelle fur (Ko-)Verlaufe

meist auf der Grundlage von

(Petri-)Netz-Modellen

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B. Thalheim

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Simulationssoftwarezur Unterstutzung der Simulation diskreter Prozesse

• Simulationssprachen - erweiterte problemorientierte Programmier-

sprachen

Deklaration simulationstypischer Datenstrukturen und Programm-

bausteine

• Simulationssysteme - vordefinierte Elementetypen, Algorithmen

entweder

• ereignisorientiert (Prozeß auf dem Niveau von Ereignissen) oder

• prozeßorientiert (Prozeß als Zusammenstellung von Teilprozessen)

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Simulationsexperimente

Ergebnisparameter: Parameter und strukturelle Relationen des Modells

(beeinflußbare Großen des modellierten Systems)

Simulationsversuch: Gesamtheit der mit einem Wertesatz der Experi-

mentierparameter erfolgten Berechnungen

Simulationsexperiment: Folge von Simulationsversuchen mit jeweils

geanderten Wertesatz der Experimentierparameter

Beispiel GPSS Ereignisbezeichner Inhalt

GENERATE Ankunft der nachsten Forderung

QUEUE Eintritt der Forderung in die Warteschlange

SEIZE Belegen einer Station

DEPART Austritt aus der Warteschlange

ADVANCE Verzogern der Bearbeitungsdauer

RELEASE Freigeben einer Einrichtung

LEAVE Verlassen des Speichers

TERMINATE Senke fur Forderungen

Validierung mit einer Stichprobenfunktion

Einschwingphase bei Start aus dem Leerzustand

Animation zur visuellen Darstellung der Dynamik des Systems

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Modellierung in der Mathematik

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Content

Information

c⃝B. Thalheim

Modellierung in der Informatik

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B. Thalheim

Engineering

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Two schemata

Model

Modelling Act

Principles

Library

Finally

Concept Topic

Content

Information

c⃝B. Thalheim

Models in Ecology

30


B. Thalheim

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Library

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Information

c⃝B. Thalheim

Modelle zur Datenerfassung in derArchologie

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B. Thalheim

Engineering

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Two schemata

Model

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Library

Finally

Concept Topic

Content

Information

c⃝B. Thalheim

Modelle zur Datenerfassung in derArchologie

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B. Thalheim

Engineering

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Two schemata

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Modelling Act

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Library

Finally

Concept Topic

Content

Information

c⃝B. Thalheim

The Creator/Actor DevelopmentDimension�� Everybody is different and thus modelling has different results

Attitude and preferences

Ability to understand, to model, to reason, to survey, to commu-

nicate with others, to analyse, to construct systems, to validate or

to verify or to test models, to use or develop documentations

Master complexity, improvements, realisation

Knowledge, skills, competency for representing world, for coping

with representations

Restricted expressivity due to restricted leads to preferring local

reasoning

Experience to cope with varieties of problem solutions through

generic problem solving

Referential solutions to be used for solution of similar problems

together with refinement of the given approach

33


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c⃝B. Thalheim

Humans are Restricted

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c⃝B. Thalheim

Actor Profiles and TopicsActors as groups of users with similar tasks, similar culture, similar

behavior and similar interests

Actor specification frame

Actor profile: ⟨actor profile name⟩Grouping criteria: ⟨characteristics of grouping of users⟩Information demand: ⟨general description⟩Utilisation pattern: ⟨general description⟩Specific utilisation: ⟨general description⟩Actor context: ⟨general description⟩

Topics are used by actors for annotating content and representing con-

cepts

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Mappings from and to the User Dimension

User schemaUser memes

s

6

+

Content schema / Content data

Concept schema /Logical theories

Topic schema /Topics landscape

query forview data

associateby utterance

understandchunks

User schemaUser memes3

?

k

Content schema / Content data

Concept schema /Logical theories

Topic schema /Topics landscape

provideview data

expressfor utterance

describe bychunks

User2Topic-Mapping: associate/express by utterance

User2Content-Mapping: query/provide view data

view data := πcontent (utterance 1 asset)

User2Concept-Mapping: understand/describe meaning by chunks

chunk := πconcept(utterance 1 infon)

Consistency by requirement

πcontent (utterance 1 asset) = πcontent (utterance 1 infon 1 semantical unit)

or ⊆, ⊂, ⊃, or ⊇

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Result of user-oriented CMS�� Not trapped in the SQL trap

Tina Musterfrau,causaluser

?

6

-userin the

DBMS trap

help !!help !!

??

?

6

?

topicwelt

concepts

searchconcept

?resultconcept

- answerform

?

answerfor search

- queryform

SQLquery

-

relationaldatabaseschema

?

parametricHERM

expressions

?

SQL queryset

)

DBMS queryrepresentation

q

?

queryinterface

�-

Searchrequest

:

� database

DBS

37


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Content

Information

c⃝B. Thalheim

Mappings from and to the User Dimension

Conceptworld

Topicmap

Contentmacrodata

Userunderstanding

Userunderstanding

Userunderstanding

Chunk Utterance

Unit Asset

Infon

View View

Chunk Utterance

Concept Symbol

ContentUser User

User

Chunk Utterance

Unit Asset

Infon

View View

Chunk Utterance

Conceptschema

Topicschema

Contentschema

Userschema

Userschema

Userschema

Chunk Utterance

Unit Asset

Infon

View View

Chunk Utterance

38


B. Thalheim

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The Activity Development Dimension�� The task is never complete: p´tamoi rh´ousi ‘the rivers flow’

Scope insight for conscious handling of restriction, capabilities, op-

portunities;

Guiding rules for convenience, for completion, refinement, and ex-

tension;

Development plans for partial delivery of work products, partial us-

age and deployment;

Theories supporting development of models;

Quality characteristics for model completion, model evolution,

model engineering

Mappings styles for mapping among abstraction layers

39


B. Thalheim

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Content

Information

c⃝B. Thalheim

Establishing a Model�� Phases

• Clarification phase

• Model construction phase

• Model experimentation phase, e.g. based on simulation methods

• Model optimisation phase

• Model validation phase

• Model application phase e.g. for decision making

40


B. Thalheim

Engineering

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Model

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Concept Topic

Content

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c⃝B. Thalheim

Modellierungszyklus

ErgebnisseErgebnisverwertung

- �

�

Modellvalidierung

Computermodell

Modell-verifi-zierung

Programmausfuhrung

?

FormalesModell

Programmierung

?

EmpirischeDaten

Datenauf-bereitung /

-formalisierung

-

KonzeptionellesModell

Formalisierung/Algorithmierung(generalisierende Abstraktion)

?

Realsystem

Problem- und Zielformulierung

Struktur- und FunktionsanalyseSystemdefinition

(isolierende Abstraktion)?

?

?

41


B. Thalheim

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c⃝B. Thalheim

Nutzung von Modellen i.a.domains application model implementation

A Φ(A) Ψ(G) G Φ(G) Ψ(B) B Φ(B)

purpose description communication√ √

(√)

√ √

purpose description understand, discover√

(√)

√ √!!

√!!

purpose understand optimise√ √ √ √ √ √ √

purpose hypothesis development verification√ √ √ √ √

purpose description alternate, change√ √ √ √ √

purpose instantiate simulate√ √ √ √

(√)

purpose instantiate evolve theory√ √ √ √ √

42


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The Partner Development Dimension�� Models tend to be too large for a singleton person!

Roles during activities such as stakeholder, developer, consultant,

supplier, contractor, documentation developers, or finally business

user

Collaboration partnership based on communication acts, coop-

eration business processes, and coordination agreements

Teamwork during all activities with separation of different tasks;

Historical people such as teachers, legacy (better heritage) devel-

opers, coders, ...

Builders of their products based on the current solution

43


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Collaboration Based on a CommonlyAgreed and Understood Language�� Forget about religious wars about the language choice

Inherent complexity of languages with pattern-based transforma-

tion to realisation languages

Ambiguity of expressions requires context extraction and injection;

variety of meanings as modularisation, context abstraction and pri-

vacy protection

Choice among (partially) equivalent expressions depending

on emphasis, appropriate appearance reflecting reality within the

understanding of the user community�� Variety of models depending on preferences, users, usages, ...

Partially defined semantics for economy of utterances

Language-dependent culture of representation

Capacity and appropriatedness of languages for certain aspects

within the application�� Model ensembles, model suites, GLaV model sets

44


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Modelling Ingredients = Languages +Restrictions + Negotiation +

Methodologies

Languages with syntactics, semantics, pragmatics

inductive expression formation based on alphabets

behaviour defined on expressions

Restrictions depending on logics

deontic, epistemic, modal, belief, preferences

shortcuts, ambiguities, ellipses

inherent language semantics

classes of constraints versus sets of real-life constraint

Negotiation by identification and analysis of the barriers(strategic, psychological, legal, and structural)to management or resolution of conflict

development of strategies to overcome these barriers

Methodology of development based on pragmatism, paradigms

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Variables in modelling�� e.g., static, deterministic, discrete, complex, intertwined

• Kind of system and its equations

• Predictability of behaviour

• Value domain

• Complexity of model

• Correlation within model

static dynamic

Evolution of the system

deterministic stochastic

Behaviour of variables

discrete continuous

Values for variables

one-dimensional complex, many-dimensional

Dimension of variables

independent correlated

Dependence among variables

46


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Constituents of Modelling�� Development of decisions and strategies

Aims of the integrated model and target outcome

criteria of fitting

similarity models

Integration and mapping of models depending

on their correlation and cohesion

Detection, description, experimentation of laws with main

players, stability factors, entailers

Evolution of model ecosystems with explicit extraction of con-

trol parameters that effect or drive evolution

47


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c⃝B. Thalheim

Establishing a Model�� Phases

• Clarification phase

• Model construction phase

• Model experimentation phase, e.g. based on simulation methods

• Model optimisation phase

• Model validation phase

• Model application phase e.g. for decision making

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Supporting Facilities

Blackboard for explicit treatment of open issues to be resolved

Obligation handler for explicit intermediate storage of ques-

tions/activities left open at a certain activity for resolution at a

later activity

Decision tracker for explicit recording of decision made

Alternative schemata for later revision of a schema

Schema equivalence recorder for later choice of another schema

that is is equivalent to the current one

Incompleteness manager for explicit incomplete elements in a

model

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Primitive Activitiesaccording to kinds of data abstraction

(classification, instantiation; aggregation, decomposition;

generalization, specialization)

Operations: selection / union,

partition / union,

nest / unnest,

projection / product (or join)

Composition constructor C, new name t, types t1, ..., tn

compose(t1, ..., tn, C, t)

Decomposition for a type t, terms e0, e1, ..., en on t, names N0, ..., Nn

decompose(t, e0, e1, ..., en, N0, N1, ..., Nn)

Extension special composition

t′′ = extent(t, t′) extend t by t′ in component c

Initialization

generate(t) for abstract t

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Computational RefinementGiven two abstractions M,M∗, refinement is based on

refinement of states

states of interest S, S∗, correspondence between the states of interest

abstract computation segments τ1, ...., τm on M and σ1, ...., σn on M∗

(m,n)-refinement

locations of interest

equivalence relation ≡ on locations of interest

M∗ is a correct refinement of M if

there for each M∗-run S∗0 , ...., S

∗k , ... there is an M-run and sequences i0 < i1 < ....

and j0 < j1 < ... such that i0 = j0 = 0 Sik ≡ S∗jk

for each k and either

• both runs terminate and their final states are the last pair of equivalent states,

or

• both runs and both sequences are infinite.

Complete refinement: M correct refinement of M∗ and M∗ correct refinement

of M

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Kinds of Refinement

Inside-out refinement: Inside-out refinement uses the current IS for extending

it by additional part. These parts are hocked onto the current specification

without changing it.

Top-down refinement: Top-down refinement uses decomposition of functions

in the vocabulary and refinement of rules. Additionally, the IS may be extended

by functions and rules that are not yet considered.

Bottom-up refinement: Bottom-up refinement uses composition and gener-

alisation of functions and of rules to more general or complex. Bottom-up

refinement also uses generation of new functions and rules that are not yet

considered.

Modular refinement: Modular refinement is based on parqueting of applica-

tions and separation of concern. Refinement is only applied to one module and

does not affect others. Modules may also be decomposed.

Mixed skeleton-driven refinement: Mixed refinement is a combination of re-

finement techniques. It uses a skeleton of the application or a draft of the

architecture. This draft is used for deriving plans for refinement. Each com-

ponent or module is developed on its own based on top-down or bottom-up

refinement.

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Generic Refinement Steps and TheirCorrectness

Consistencyconditions

DBMS specificationassumptions

Refinement step

Derivation ofspecific

refinementsteps

?

- �

?

Refinement pattern

Generic refinement step

Perspectivesand styles

Developmentcontract

Derivation ofgeneric

refinementsteps

?

?

- �

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Often Neglected Activities

• Validation and verification activities

proof of properties, correctness, completeness

• Development of coherent constraint sets

• Detection of concepts and conceptions

• Quality management activities

e.g., robustness, modality, completeness, error forgiveness

• Transformation activities

compilation, interpretation, mapping

collaboration

abstraction

• Representing causal dependencies

54


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Componentisation

Worki�)

Fixed AssetAssignmentFixed AssetAssignment

InventoryAssignmentInventory

Requirement

1-q

Asset

M6�

Billing

Time Entry 1-q

Invoice

WorkTask

WorkTaskAssignment 1

-q

?N

Partyi�)

PartyAssetAssignment

PartyAssetAssignmentRequirement

?N

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Orthogonal Subschemata

PersonOrganization Product

Fond

PersonnelManagement

6 6

� -SupplierCustomer

Order

6 6

6

Delivery � Billing

?

Address

� Production

��

6

Budget

6

BudgetDepartment

56


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Seven Myths of Formal Methods�� Anthony Hall

(1) Formal methods can guarantee that software is perfect.

(2) Formal methods are all about program proving.

(3) Formal methods are only useful for safety-critical systems.

(4) Formal methods require highly trained mathematicians.

(5) Formal methods increase the cost of development.

(6) Formal methods are unacceptable to users.

(7) Formal methods are not used on real, large scale software.

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Seven + Seven Myths of Formal Methods�� Dines Bjørner

(8) Formal Methods delay the development process.

(9) Formal Methods are not supported by tools.

(10) Formal Methods mean forsaking traditional engineering design

methods.

(11) Formal Methods only apply to software.

(12) Formal Methods are not required.

(13) Formal Methods are not supported.

(14) Formal Methods people always use Formal Methods.

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Formalisation: Ten Commandments�� Jonathan P. Bowen & Michael G. Hinchey

(1) Thou shalt choose an appropriate notation.

(2) Thou shalt formalise but not overformalise.

(3) Thou shalt estimate costs.

(4) Thou shalt have a formal methods guru on call.

(5) Thou shalt not abandon thy traditional development methods.

(6) Thou shalt document sufficiently.

(7) Thou shalt not compromise thy quality standards.

(8) Thou shalt not be dogmatic.

(9) Thou shalt test, test, and test again.

(10) Thou shalt reuse.

Another three?

(1) Thou shall meet intentions of developers, ...

(2) Thou shall provide a usable notation, i.e. for verification, validation,

explanation, elaboration, and evolution.

(3) Thou shall be robust against misinterpretation, errors, ...

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Formalisation: Kinds and NeedsFormal development: a software development which uses one or more formal techniques

and it may then use these in a spectrum.

Formal software Development: a software development in which specifications are

expressed in a language with a formal syntax so that all specifications can be judged well-

formed or not, with a formal semantics so that all well-formed specifications have a precise

meaning, and a (relatively complete) proof system such that one may be able to reason over

properties of specifications or steps of formally specified developments from a more abstract

to a more concrete step.

Additionally a formal technique may be a calculus which allows developers to calculate, to

refine “next”, formally specified development steps from a preceding, formally specified step.

Systematic (formal) development: a software development which formally specifies

whenever something is specified, but which does not (at least only at most in a minor of

cases) reason formally over steps of development.

Rigorous (formal) development: a software development which formally specifies

whenever something is specified, and which formally express (some, if not all) properties

that ought be expressed, but which does not (at least only at most in a minor number of

cases) reason formally over steps of development, that is, verify these to hold, either by

theorem proving, or by model checking, or by formally based tests.

Formal (formal) development: a software development which formally specifies

whenever something is specified, which formally expresses (most, if not all) properties that

ought be expressed, and which formally verifies these to hold, either by theorem proving, or

by model checking, or by formally based tests.

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Kind of Abstraction

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Choices for Specification: Modularisation�� Implementation and localisation abstraction

data, functional, and control decomposition

Explicit modularisation: skeleton-based modelling and architec-

turing

interfaces and collaboration of components

side-effect free computation and controlled effect on partners

Implicit modularisation: through name spaces�� Advantages

separation of concerns, discovery of basic concepts, validation and ver-

ification of development, efficiency of tool support, scoped changes,

evolution and extension, analysability, conservativeness, incrementality,

testability �� Disadvantages

does not support agile development and brute-force prototyping

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Choices for Development AlternativesObject-expanded or class-separated depending on the modelling language,

e.g. XML choices• Venetian blind (full expansion of objects)

• Russian doll (DTD style)

• Salami slice (similar to ER diagrams)Development strategy as 3-dimensional decision chart

6

-

Controlleddevelopment

(U,I)

Development direction (B,J,T)

Modularisation (C,V,M)

IMT

IVT

ICT

UCB

UVT

UCT

UMT

IMJ

IVJ

ICJUCJ

ICB

UVBUVB

IMBUMB

UMJ

UVJ

B: bottom-up

T: top-down

J: jojo

M: modular

V: view-based

C: central

U: uncontrolled

I: inside-out

Dividing ridge for object (entity) typesLimiting expressiveness e.g., binarisation

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Choices for Specification: Perspectives andStyles

Structure-oriented perspective: structural description of the IS

+ semantic perspective

Behavior-oriented perspective: behavior of the IS during its life-

time

event approaches, Petri-net approaches, predicate transition sys-

tems

Process-oriented perspective operation of the system�� Advantages

development methodology and scheduling, results in development

strategies (top-down, inside-out, ...), analysability�� Disadvantages

depends on whether a system will have this perspective

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Coupling

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Principles (Reminder)�� 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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Development of Modelling Resultsresult of modelling: (c, d, (ρ, θ, ψ), g, w)

• representation concept c

• things d under consideration

• with restrictions for their applicability ρ

• with a rigidity or modality θ

• with a confidence ψ on their validity

• based on a common understanding of a group g

• within their world w or culture

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Application to Integration Problems

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Model Library: General Frame forConstruction and Deployment of Models

Founding concepts: base conceptions/concepts (scope, expres-

sions, concept space organisation, quantification/measurement),

namespace/ontology/carrier, definitions (state, intrinsic, object, in-

teraction descriptors and depictors), cargo

Structure and function: incremental?, facets (topol-

ogy/geometry, state, interaction, causal), correspondence (analogy,

...)

Application context: application domain, empirical scope of the

model, correspondence, laws

Meta-model: basement, paradigms and theories; status in the

application; context; proneness for paradigmatic evolution (within

the epistemological profile of stakeholders); abstraction level, scale

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Model Library: Main Properties

Mapping property

Truncation property

Pragmatic property

Extension property

Distortion property

Idealisation property

Carrier property

Added value property

Purpose property

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Model Library: Goal, Purpose, Function

see Art of conceptual modelling

Goal

Purpose primary/secondary/no-go

Function of the model in the scientific process; tasks for the model

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Model Library: Appropriateness of anArtifact to Become a Model

see Art of conceptual modelling (Criteria for Appropriateness of an

Artifact to Become a Model)

Adequacy of a model its potential for the goals

Fitness for its purpose

Usefulness for deploying

Assumptions made for the model and essential in the area

Capacity of the model

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Model Library: Application Domain of aModel

Application discipline scientific discipline and branch

Problem to be solved with the model

Successful applications of the model within the discipline

Extreme/surprising applications of the model in other application disci-

plines, border disciplines

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Model Library: The Context of a Model

Community of practice stakeholders with their roles

• modeller, developer

• user, applying the model

• teaching, explanation

• opponents of the modelOther potential and actual applications

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Model Library: Model WorldHorizontal and vertical decomposition

Other versions detailedness, fineness/resolution

Relation to other models

• refinements, extensions and simplifications of the model

• integrateablity, combinability, mutual exclusion, contradictions

• ...

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Model Library: Carrier Dimension forModels

Language used for the model

Specific styles for description

Pattern reused from other models or shared with other models

Alternative specification

Alternative specification

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Model Library: The “To Model”Dimension

Refined techniques for “to model”

Model refinement techniques

Data quality dependence

Specific techniques applied for this model

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Model Library: Model Properties 1/4Name Description

Generic

Type

The generic model type serves as a generic description framework which covers characteristics

of a wide spread of models.

Name This characteristics specifies the textual name of the model.

Reference Primary Reference / Secondary Reference: This characteristics lists references used for classi-

fying the model respectively from where the characteristics of the model features are surveyed.

The specification can distinguish between primary references wherein the model is represented

and secondary references wherein additional statements about specific characteristics of the

model are described.

Origin The origin informs about the classification of the person(s) who have developed the model.

In this regard, mainly “science” and “practice” can be distinguished.

Respon-

sibility

Personal Responsibility / Organizational Responsibility for Modelling: This characteristics

describes the persons as well as organizations that developed the model.

Access The access specifies the accessibility to the model by third parties. If the model is completely

obtainable over usual ways of librarianship the access is classified as “open”?. The access

is “closed”, if the responsible person(s) or institution provides no possibility for using and

recognizing the model by third parties. If the access is neither open nor closed the access

is classified as “limited”?. This is the case, e.g., if the model can be purchased as stand-

alone product. If the access to the model is closed the information of all criteria is based on

statements from the specified literature.

... ...

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.... ....

Tool Sup-

port

This characteristics describes whether the model can be automatically used by a software

tool or whether the model is only available in paper or digital copy.

Domain The domain describes the intended field of the model’s application from perspective of the

person(s) or institution responsible for developing the model.

Modelling

Language(s)

The language criteria state the modelling language(s) used to represent the model.

Modelling

Framework

This characteristics describes whether a modelling framework is part of the model. A

framework can structure relevant elements esp. diagrams of a model and their relationships

at a higher level of abstraction. This reduces complexity and provides an overview of

elements and relationships within the model.

Number of

Diagrams

This characteristics specifies the number of diagrams of the model as general size attribute.

Number of

Views

This characteristics specifies the number of views/perspectives of the model as general

size attribute.

Process-

related Size

As a process-related metric, the number of process steps within represented process dia-

grams is stated. The mentioned size of smaller models (¡30) is counted, the size of bigger

models is partly estimated and rounded off to full decade. If the access to the model is

closed the information is based upon statements of given references.

.... ...

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.... ....

Organization-

related

Size

As an organization-related metric, the number of organizational units within represented

organization diagrams is stated. The mentioned size of smaller models (<30) is counted,

the size of bigger models is partly estimated and rounded off to full decade. If the access

to the model is closed the information is based upon statements of given references.

Data-

related

Size

As a data-related metric, the number of entity types within represented data diagrams is

stated. The mentioned size of smaller models (<30) is counted, the size of bigger models

is partly estimated and rounded off to full decade. If the access to the model is closed the

information is based upon statements of given references.

Function-

related

Size

As a function-related metric, the number of functions within represented function diagrams

is stated. The mentioned size of smaller models (<30) is counted, the size of bigger models

is partly estimated and rounded off to full decade. If the access to the model is closed the

information is based upon statements of given references.

Output-

related

Size

As a output-related metric, the number of outputs within represented output-oriented dia-

grams is stated. The mentioned size of smaller models (<30) is counted, the size of bigger

models is partly estimated and rounded off to full decade. If the access to the model is

closed the information is based upon statements of given references.

Construction

Method

This characteristics states the modelling concept used by the responsible person(s) or in-

stitution for developing the model.

.... ....

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.... ....

Construction

Method

This characteristics states the modelling concept used by the responsible person(s) or in-

stitution for developing the model.

Evaluation

Method

This characteristics describes the used methods for evaluating the model by the person(s)

or institution responsible for developing the model or by third parties. Evaluation methods

are only considered, if they are explicitly intended for model evaluation by the evaluator.

Application

Method(s)

This characteristics describes the known method respectively concept for applying the

model.

Reuse

and Cus-

tomization

This characteristics lists concepts for reusing and customizing of model elements in the

scope of the models application.

Use

Case(s)

The use case(s) states how often the model was applied to construct an application model.

Abstract This characteristics includes a brief verbal description of the model.

c⃝(for IS proposal) http://rmk.iwi.uni-sb.de

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Model Library: Viability of Models�� 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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Model Library: General PropertiesGenealogical tree and evolution of the model

Abstraction and simplification applied to the model

Robustness against data, beliefs, foundations, error tolerance, sensitivity

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Model Library: SWOT Analysis of theModel

as a summary

Strengths

Weaknesses

Opportunities

Threats

resulting restrictions, no-go-applications

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Rahmen zur Modellaufnahme

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Modellkatalog

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SWOT-Analyse-Rahmen

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Finally

89

Thank you!

[email protected]

Concept Topic

Content

Information

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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.

• 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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Publications on Model Suites, Evolution,Migration

• A. Dahanayake and B. Thalheim. Co-evolution of (information) system models. In EMMSAD

2010, volume 50 of LNBIP, 314–326. Springer, 2010.

• 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.

• M. Klettke and B. Thalheim. Evolution and migration of information systems. In The Handbook

of Conceptual Modeling: Its Usage and Its Challenges, chapter 12, 381–420. Springer, Berlin,

2011.

• B. Neumayr and M. Schrefl und B. Thalheim. Modeling techniques for multi-level abstraction.

In The Evolution of Conceptual Modeling, volume 6520 of Lecture Notes in Computer Science,

68–92, Berlin, 2011. Springer.

• B. Thalheim. Model suites. In H. Jaakkola, editor, Selected Topics on Distributed Disaster

Management: Towards Collaborative Knowledge Clusters., 108 – 128. Tampere University

Press, Porin yksikko, 2008.

• B. Thalheim. The conceptual framework to multi-layered database modelling. In Proc. EJC,

118–138, Maribor, Slovenia, 2009.

• B. Thalheim. Model suites for multi-layered database modelling. In Information Modelling

and Knowledge Bases XXI, volume 206 of Frontiers in Artificial Intelligence and Applications,

116–134. IOS Press, 2010.

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Publications on Tool-Based Development• M. Albrecht, M. Altus, E. Buchholz, H. Cyriaks, A. Dusterhoft, J. Lewerenz, H. Mehlan, M. Steeg,

K.-D. Schewe, and B. Thalheim. RADD - Rapid application and database development. Read-

ings - Main papers published in the RADD project. CAU Kiel, Department of Computer Science,

http://www.is.informatik.uni-kiel.de/∼thalheim/indeeerm.htm, 1998.• G. Fiedler, H. Jaakkola, T. Makinen, B. Thalheim, and T. Varkoi. Co-design of web information systems

supported by SPICE. Information Modelling and Knowledge Bases, XIX, 2009.• H. Jaakkola and B. Thalheim. A framework for high quality software design and development: A

systematic approach. IET Software, 2010. to appear.• H. Ma, K.-D.Schewe, B. Thalheim, and J. Zhao. View integration and cooperation in databases, data

warehouses and web information systems. Journal on Data Semantics, LNCS 3730, 213–249, 2005.• M. Steeg. RADD/raddstar - A rule-based database schema compiler, evaluator, and optimizer. PhD

thesis, BTU Cottbus, Computer Science Institute, Cottbus, October 2000.• B. Thalheim. Entity-relationship modeling – Foundations of database technology. Springer, Berlin,

2000.• B. Thalheim, K.-D. Schewe, and Hui Ma. Conceptual application domain modelling. In APCCM,

volume 96 of CRPIT, 49–57. Australian Computer Society, 2009.• B. Thalheim. Co-design of structuring, functionality, distribution, and interactivity of large information

systems. Technical Report 15/03, BTU Cottbus, Computer Science Institute, Cottbus, September

2003. 190pp.• B. Thalheim. Conceptual modeling in information systems engineering. In J.Krogstie and A. Lothe,

editors, Challenges to Conceptual Modelling, 59–74, Berlin, 2007. Springer.

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Publications on Pattern Development

• T. Feyer, K.-D. Schewe, and B. Thalheim. Conceptual design and development of information

services. In Proc. ER’98, LNCS 1507, Springer, 1998, 7–20. Springer, Berlin, 1998.

• T. Feyer and B. Thalheim. Many-dimensional schema modeling. In ADBIS 2002, LNCS 2435,

305–318. Springer, 2002.

• T. Feyer and B. Thalheim. A model for defining and composing interaction pattern. In

EJC’2002, volume Information Modelling and Knowledge Bases XIV, 277–289, 2002.

• Hui Ma, K.-D. Schewe, and B. Thalheim. Modelling and maintenance of very large database

schemata using meta-structures. In UNISCON, volume 20 of Lecture Notes in Business Infor-

mation Processing, 17–28. Springer, 2009.

• K.-D. Schewe and B. Thalheim. Development of collaboration frameworks for web informa-

tion systems. In IJCAI’07 (20th Int. Joint Conf on Artificial Intelligence), Section EMC’07

(Evolutionary models of collaboration), 27–32, Hyderabad, 2007.

• B. Thalheim. Many-dimensional database modeling on the basis of application frameworks.

Technical Report Preprint I-08-2000, Brandenburg University of Technology at Cottbus, Institute

of Computer Science, 2000.

• B. Thalheim. The person, organization, product, production, ordering, delivery, invoice, ac-

counting, budgeting and human resources pattern in database design. Technical Report I-07-

2000, Computer Science Institute, Brandenburg University of Technology at Cottbus, 2000.

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Publications on Component Development

• A. Dusterhoft and B. Thalheim. Linguistic based search facilities in snowflake-like database schemes.

Data and Knowledge Engineering, 48:177–198, 2004.

• T. Feyer and B. Thalheim. Component-based interaction design. In EJC’2003, volume Information

Modelling and Knowledge Bases XV, 19 – 36, 2003.

• G. Fiedler and B. Thalheim. An approach to conceptual schema evolution. Technical report,

Christian-Albrechts-Universitat Kiel, 2007.

• K.-D. Schewe and B. Thalheim. Component-driven engineering of database applications. In

Markus Stumptner, Sven Hartmann, and Yasushi Kiyoki, editors, Third Asia-Pacific Conference

on Conceptual Modelling (APCCM2006), volume 53 of CRPIT, 105–114, Hobart, Australia, 2006.

ACS.

• P. Schmidt and B. Thalheim. Component-based modeling of huge databases. In ADBIS’2004,

LNCS 3255, 113–128, 2004.

• B. Thalheim. Component construction of database schemes. In Proc. ER’02, LNCS 2503, 20–34.

Springer, 2002.

• B. Thalheim. Component development and construction for database design. Data and Knowledge

Engineering, 54:77–95, 2005.

• B. Thalheim. Engineering database component ware. In TEAA’06 post proceedings, LNCS 4473,

1–15, Berlin, 2007. Springer.

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Publications on Genericity• A. Bienemann. A generative approach to functionality of interactive information systems. PhD

thesis, CAU Kiel, Dept. of Computer Science, 2008.

• A. Bienemann, K.-D. Schewe, and B. Thalheim. Towards a theory of genericity based on government

and binding. In Proc. ER’06, LNCS 4215, 311–324. Springer, 2006.

• A. Binemann-Zdanowicz, B. Thalheim, and B. Tschiedel. Storyboarding for adaptive content gen-

eration for e-learning web services. In Computer Science Report I-10/2003, Brandenburg University

of Technology at Cottbus, 2003.

• A. Binemann-Zdanowicz. Towards generative engineering of content-intensive applications. In

Proc. Principles of Software Engineering Conference (PRISE 2004), 41–49, 2004.

• M. Klettke. Reuse of database design decisions. In P. P. Chen, D. W. Embley, J. Kouloumdjian,

S. W. Liddle, and J. F. Roddick, editors, Proc. Advances in Conceptual Modeling, LNCS 1727,

213–224. Springer, Berlin, 1999.

• T. Moritz. Visuelle Gestaltungsraster interaktiver Informationssysteme als integrativer Bestandteil

des immersiven Bildraumes. PhD thesis, HFF Berlin-Babelsberg, 2006.

• B. Thalheim. The conceptual framework to multi-layered database modelling. In Proc. EJC,

118–138, Maribor, Slovenia, 2009.

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Publications on Co-Design• Dusterhoft, A., Thalheim, B.: SiteLang: Conceptual Modelling of Internet Sites. Proc. ER’2001, LNCS 2224,

179 - 192. Application to webservices• Feyer, Th.; Thalheim, B.: E/R Based Scenario Modelling for Rapid Prototyping of Web Information Services.

Proc. WWWCM’99, 253 - 263. Application to webservices generation• G. Fiedler, H. Jaakkola, T. Makinen, B. Thalheim, and T. Varkoi. Co-design of web information systems

supported by SPICE. Information Modelling and Knowledge Bases, XX:123–138, 2009.

• Goldin, D., Srinivasa, S., Thalheim, B.: IS=DBS + Interaction: Towards principles of information system

design. Proc. ER 2000, LNCS 1920, 140 - 153. The theoretical foundation• Klettke, M.: Reuse of database design decisions. Proc. REIS’2000, LNCS 1727, 213-224. Reuse structures

and intelligently acquire integrity constraints• Lewerenz, J., Schewe, K.-D., Thalheim, B.: Modelling data warehouses and OLAP applications by means of

dialogue objects. Proc. ER’1999, LNCS 1728, 354-368. OLAP in a consistent, powerful and simple way• K.-D. Schewe and B. Thalheim. The co-design approach to web information systems development. Interna-

tional Journal of Web Information Systems, 1(1):5–14, March 2005.

• Schewe, K.-D.; Thalheim, B.: Towards a theory of consistency enforcement. Acta Informatica, 36, 1999,

97-141. Instead of falling into the traps of rule triggering systems• Steeg, M; Thalheim, B.: Conceptual Database Application Tuning. Proc. SCI’2000, 226-231. Tune instead

of normalize• Thalheim, B.: Entity-Relationship Modelling - Foundations of Database Technology. Springer, Berlin, 2000.

The HERM “bible”• Thalheim, B.: Logics and Database Modelling. Proc. ICLP ‘99, MIT Press, 6-21. The relationship to logics• Thalheim, B.: Codesign of database systems and interaction - Thin and consistent UML. Proc. OTS’2000,

1-17. Codesign - the ultimate basis for best practices UML

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Publications on Web IS Engineering• A. Binemann-Zdanowicz. Sitelang::edu - towards a context-driven e-learning content utilization model. In Proc.

SAC’2004 (ACM SIGAPP), Nicosia, Cyprus, March 2004, 924–928. ACM, 2004.• A. Dusterhoft and B. Thalheim. Linguistic based search facilities in snowflake-like database schemes. Data and

Knowledge Engineering, 48:177–198, 2004.• T. Feyer, K.-D. Schewe, and B. Thalheim. Conceptual design and development of information services. In Proc.

ER’98, LNCS 1507, Springer, 1998, 7–20. Springer, Berlin, 1998.• R. Kaschek, K.-D. Schewe, B. Thalheim, and Lei Zhang. Integrating context in conceptual modelling for web

information systems, web services, e-business, and the semantic web. In WES 2003, LNCS 3095, 77–88. Springer,

2003.• T. Moritz, R. Noack, K.-D. Schewe, and B. Thalheim. Intention-driven screenography. In Proceedings ISTA

2007, volume LNI 107, 128–139, 2007.• T. Moritz, K.-D. Schewe, and B. Thalheim. Strategic modelling of web information systems. International

Journal on Web Information Systems, 1(4):77–94, 2005.• K.-D. Schewe and B. Thalheim. Conceptual modelling of web information systems. Data and Knowledge

Engineering, 54:147–188, 2005.• K.-D. Schewe and B. Thalheim. Pragmatics of storyboarding for web information systems: Usage analysis. Int.

Journal Web and Grid Services, 3(2):128–169, 2007.• K.-D. Schewe and B. Thalheim. Personalisation of web information systems - a term rewriting approach. Data

and Knowledge Engineering, 62(1):101–117, 2007.• B. Thalheim. Readings in fundamentals of interaction in information systems. Reprint, BTU-Cottbus, acces-

sible through http://www.is.informatik.uni-kiel.de/∼thalheim, Collection of papers by C. Binder, W. Clauß, A.

Dusterhoft, T. Feyer, T. Gutacker, B. Heinze, J. Lewerenz, M. Roll, B. Schewe, K.-D. Schewe, K. Seelig, S.

Srinivasa, B. Thalheim, 2000.• B. Thalheim and A. Dusterhoft. Sitelang: Conceptual modeling of internet sites. In H. S. Kunii, S. Jajodia, and

A. Sølvberg, editors, ER, volume 2224 of LNCS, 179–192. Springer, 2001.

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Publications on Database Theory• E. Borger and B. Thalheim. A method for verifiable and validatable business process modeling.

In Software Engineering, volume 5316 of Lecture Notes in Computer Science, 59 – 115. Springer,

2008.

• D. Goldin, S. Srinivasa, and B. Thalheim. IS = DBS + interaction - towards principles of

information systems. In A. H. F. Laender, S. W. Liddle, and V. C. Storey, editors, ER, volume

1920 of LNCS, 140–153. Springer, 2000.

• H.-J. Lenz and B. Thalheim. A formal framework of aggregation for the OLAP-OLTP model.

Journal of Universal Computer Science, 15(1):273 – 303, 2009.

• K.-D. Schewe and B. Thalheim. Reasoning about web information systems using story algebra.

In ADBIS’2004, LNCS 3255, 54–66, 2004.

• K.-D. Schewe and B. Thalheim. Fundamental concepts of object oriented databases. Acta

Cybernetica, 11(4):49–81, 1993.

• K.-D. Schewe and B. Thalheim. Readings in object-oriented databases. Reprint, BTU-Cottbus,

accessible through http://www.is.informatik.uni-kiel.de/∼thalheim, Collection of papers by C.

Beeri, K.-D. Schewe, J.-W. Schmidt, D. Stemple, B. Thalheim, I. Wetzel, 1998.

• O. Seleznev and B. Thalheim. Average case analysis in database problems. Methodology and

Computing in Applied Probability, 48:177–198, 2003.

• B. Thalheim. Entity-relationship modeling – Foundations of database technology. Springer,

Berlin, 2000.

• B. Thalheim. Model suites. In 2nd International Workshop on Knowledge Cluster Systems,

20–40. IOS Press, 2008.

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