A Historical Perspective on Conceptual Modelling

1. A Historical Perspective on Conceptual Modelling (Based on an article and presentation by Janis Bubenko jr., Royal Institute of Technology, Sweden. June 2005) Pensum: A01: Janis A. Bubenko jr: From Information Algebra to Enterprise Modelling and Ontologies – a Historical Perspective on Modelling for Information Systems in Conceptual Modelling in Information Systems Engineering. Krogstie, John; Opdahl, Andreas Lothe; Brinkkemper, Sjaak (Eds.) Lecture 1: Introduction

2. Conceptual Modelling • Definition: • represents 'concepts' (entities) and relationships between them. • May be used for enterprise models, problem analysis requirements and design specification. • Primarily diagrammatic (2-dimensional diagrams). • The languages used for modeling have a limited vocabulary. • The languages used are originally meant to be generally applicable (and not for a specific domain). Some exception e.g. using so-called domain specific modeling techniques. Lecture 1: Introduction

3. Focus of early attemps • What were modelled were data and operations on the data. • There was a focus on representing the domain in strict, formal, computer-independent terms. • Data were modelled using abstract concepts. Lecture 1: Introduction

4. Modelling during four+ decades • Extended scope • Standardisation efforts Participation and understanding 2005 The search for a common framework 90s Refinement, models and extensions Pioneering work concepts 80s 70s 60s Lecture 1: Introduction

5. Modelling during four+ decades Young & Kent, 1958, CODASYL, 1963, Langefors 1965 • Extended scope • Standardisation efforts Participation and understanding Database Models 2005 The search for a common framework 90s Refinement, models and extensions Pioneering work concepts 80s 70s 60s Lecture 1: Introduction

6. Young and Kent (1958)“Abstract Formulation of Data Processing Problems” • … a way of designing different alternative implementations • Information set/item • Defining relationship • Producing relationship • Conditions • Temporal aspects Lecture 1: Introduction

7. CODASYL Development Committee:An Information Algebra (1962) The goal of this work is to arrive at a proper structure for a machine-independent problem-defining language at the systems level of data processing. … It should help the information processing community to clarify, understand the fundamental and essential features of data processing considerations. …With current programming languages the problem definition is buried in the rigid structure of an algorithmic statement of the solution, and such a statement cannot readily be manipulated. Source: CACM, Vol.5, No. 4, April 1962, pp. 190 - 204 CODASYL: Conference on Data System Languages Lecture 1: Introduction

8. The Scandinavian School: Langefors • the infological realm: where data processing problems were expressed. • the datalogical realm: design and analysis of a information processing system. • the “elementary message” – the smallest element that could certain any meaning. e = <s, a, v, t> s system point a attribute v value t time Lecture 1: Introduction

9. Modelling during four+ decades ANSI/X3/SPARC, IFIP Workinggroups • Extended scope • Standardisation efforts Information System Models Participation and understanding Database Models 2005 The search for a common framework 90s Refinement, models and extensions Pioneering work concepts 80s 70s 60s Lecture 1: Introduction

10. The period 1970-80 ”refinement and extensions" • The 1975 ANSI/X3/SPARC (Standards Planning and Requirements Committee) report: the three schema approach • External • Conceptual • Internal • IFIP WG 2.6 series: "Modelling in Database Management Systems” (1974) • IFIP TC 8 on Information Systems (1976) Lecture 1: Introduction

11. ANSI/X3/SPARC, 1975 • The three-schema approach offers three types of schemas with schema techniques based on formal language descriptions: • External schema for user views • Conceptual schema integrates external schemata • Internal schema that defines physical storage structures User view Neutral view Computer view • The framework attempted to permit multiple data models to be used for external schemata. Lecture 1: Introduction

12. IFIP Working Groups • IFIP: International Federation for Information Processing, an umbrella organisation for national societies working in the field of information technology. • IFIP WG 2.6 series: "Modelling in Database Management Systems” (1974). • IFIP TC 8 on Information Systems (1976). Lecture 1: Introduction

13. Significant issues, insights and proposals during the 70s • An "object" and the "name of an object" are different things. • Binary vs. Relational models. • Specialisation and generalisation, inheritance. • Distinction between types, sets, and instances. • Constraints and deduction. • The temporal dimension. • Data Model Based Data Base Management Systems. • Graphical query languages. • In summary, most of the essential basic concepts of modelling were invented and presented during the seventies. Lecture 1: Introduction

14. Modelling during four+ decades Business rule modelling • Extended scope • Standardisation efforts Information System Models Participation and understanding Database Models 2005 The search for a common framework 90s Refinement, models and extensions Pioneering work concepts 80s Temporal aspects, Semantic Modelling 70s 60s Lecture 1: Introduction

15. Ambitions of the 80’s • To understand better and improve parts of existing methods and tools. • To harmonise different notions and methods. • To enhance the requirements capture and validation stage of the systems life-cycle. • To provide computerised assistance to the process of developing a specification. • To pay attention to human, cognitive, linguistic and social aspects of IS. Lecture 1: Introduction

16. Modelling research in the 80’s • Improving the expressive power of semantic data models (including abstraction mechanisms) and adding the temporal dimension. • ”semantic modelling” vs relational data modelling. • What are we modelling? The DB? The IS? The real world? • The operational vs. the deductive and temporal approach. Lecture 1: Introduction

17. Modelling during four+ decades Modelling of ”why”, Enterprise Models Business rule modelling • Extended scope • Standardisation efforts Information System Models Participation and understanding Database Models 2005 The search for a common framework 90s Refinement, models and extensions Pioneering work concepts Usereducation and participation, Userfocus, Organisationalchange 80s 70s 60s Lecture 1: Introduction

18. Modelling in the 90’s: focus onorganisational aspects,participation, and understanding Why are we modelling? How are we modelling? … "the understanding and support of i) Human activities at all levels in an organisation. • Change, be it of the product, of the process or of the organisation. • Complex user organisations, and individual users" (ESPRIT 91) Lecture 1: Introduction

19. The 90’s: Widening the scope • Interoperable systems • Semantic heterogeneity • Non-functional requirements • Business modelling/engineering • Modelling of intentions and actors • Participative modelling • ”Method knowledge”* • ”Patterns” Lecture 1: Introduction

20. Modelling during four+ decades Modelling of ”why”, Enterprise Models Business rule modelling • Extended scope • Standardisation efforts Information System Models Participation and understanding Database Models 2005 The search for a common framework Domainspecific ”ontologicalmodels” and languages 90s Refinement, models and extensions Pioneering work concepts 80s Formality vs. informal 70s 60s Lecture 1: Introduction

21. Modelling during four+ decades ANSI/X3/SPARC, IFIP Workinggroups Modelling of ”why”, Enterprise Models Young & Kent, 1958, CODASYL, 1963, Langefors 1965 Business rule modelling • Extended scope • Standardisation efforts Information System Models Participation and understanding Database Models 2005 The search for a common framework Domainspecific ”ontologicalmodels” and languages 90s Refinement, models and extensions Pioneering work concepts 80s Usereducation and participation Formality vs. informal 70s Temporal aspects, Semantic Modelling 60s Lecture 1: Introduction

22. What do you think is important to model today? Lecture 1: Introduction

23. Current Trends • Enterprise Models • Active Knowledge Models • Context • Model-Driven Development • Modelling social aspects and Communities • Semantics, Ontologies • Interoperability and Standardisation • Leveraging on developments in other fields, e.g. • AI – reasoning about knowledge, knowledge representation, uncertain knowledge • Modelling work in other engineering fields Lecture 1: Introduction

24. Conclusions • Starting from very simple well-bounded conceptual descriptions of information and database systems, modelling has evolved into less well-defined domains. • Our needs for modelling and expectations of models have evolved. • Stakeholder involvement. • Focus of dynamics, semantics, active models. • Systems that can evolve as our needs evolve. Lecture 1: Introduction

25. Enterprise Modelling • The purpose of modelling is not only IS design. • Models not only address “what?”, but also “why?”. • Integrates conceptual and process models of the business with objectives, actors, business rules and information system requirements. • Provides traceability from information system solutions to business objectives. • Improves the quality of modelling and the models by making it a “participatory” activity. Lecture 1: Introduction

26. Summary • Introduction to the course and practical information • Historical perspective of IS modelling over 4+ decades. Lecture 1: Introduction

27. Lecture 1: Introduction