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Janis A. Bubenko jr Royal Institute of Technology, Stockholm, Sweden janis@dsv.su.se

A Historical Perspective on Conceptual Modelling: from Information Algebra to Enterprise Modelling and Ontologies. Janis A. Bubenko jr Royal Institute of Technology, Stockholm, Sweden janis@dsv.su.se. “Abstract Formulation of Data Processing Problems”. Young and Kent (1958).

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Janis A. Bubenko jr Royal Institute of Technology, Stockholm, Sweden janis@dsv.su.se

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  1. A Historical Perspective on Conceptual Modelling:from Information Algebra to Enterprise Modelling and Ontologies Janis A. Bubenko jrRoyal Institute of Technology, Stockholm, Swedenjanis@dsv.su.se

  2. “Abstract Formulation of Data Processing Problems” Young and Kent (1958) • Information set/item • Defining relationship • Producing relationship • Conditions • Temporal aspects

  3. Since we may be called upon to evaluate different computers or to find alternative ways of organizing current systems it is necessary to have some means of precisely stating a data processing problem independentaly of mechanization *). Why the need for an abstract formalism? *) Young and Kent, Journal of Industrial Engineering, Nov. – Dec. 1958, pp. 471-479

  4. Why Conceptual Modelling inInformation Systems work? - to contribute to the acquisition and description of knowledge needed in the development and maintenance of information and software systems which will become, or are, active components of real world infrastructures.

  5. Modelling during four decades Participation and understanding The search for a common framework 90-ties Refinement, models and extensions 80-ties Pioneering work - concepts 70-ties 60-ties

  6. Pioneers in IS modelling: 1959-70 Young and Kent 1959 CODASYL: Information Algebra 1963 "The Scandinavian School" Langefors 1965: Theoretical Analysis of Inf.Systems USA: D Teichroew, J. Nunamaker: PSL/PSA and optimisation of Information Processing Systems

  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

  8. Entity (e) Property (q) Property value (v) Property value set (V) Coordinate set (Q) e.g. Q = (q1, q2, q3) Property space (P) of a coordinate set (Q) e.g. P=V1 x V2 x V3 Datum point of P: d = (a1, a2, a3) Line, Area, Glump, …. Information Algebra, basic concepts Every entity has exactly one datum point in a property space. A discriminatory property space for a set of entities no datum point represents more than one entity.

  9. The Scandinavian School: Langefors * the infological and the datalogical realms * the “elementary message” * the “elementary file” e = <s, a, v, t> s system point a attribute v value t time Langefors, 1963

  10. Langefors 1966

  11. Langefors 1966 (cont)

  12. The 1975 ANSI/X3/SPARC (Standards Planning and Requirements Committee) report: the three schema approach IFIP WG 2.6 series: "Modelling in Database Management Systems” (1974) IFIP TC 8 on Information Systems (1976) THE PERIOD: 1970-80”REFINEMENT AND EXTENSIONS"

  13. IFIP Technical Committee 2 on Software: Theory and Practice • Working Group 2.6 on Database (started 1974, revised later) - Started the IFIP WG 2.6 conference series: • "Modeling in Database Management Systems” • 1974 Cargese, Corsica • 1975 Wepion, France • 1976 Freudenstadt, Germany • ….etc. Abrial, Adiba, Benci, Bracchi, Codd, Date, …. Delobel,Gardarin, Falkenberg, …. Langefors, Neuhold, Nijssen, Olle, ….. Senko, Spaccapietra, Sundgren, Tsichritzis, Wiederhold,...

  14. Jean-Raymond Abrial: ”Data Semantics”(1974) Influenced by: GDBMS, Codd’s Relational Model, AI-techniques, … Binary model sex/personofsex age/personofage person sexp number • Schema: fact types, rules • Rules: constraints, • derivation rules • Internal vs external names spouse/ spouse children/parents R4=rel(person, person, parents = afn(2,2), children = afn(0, ∞))

  15. A sample NIAM schema (Nijssen) * * Source: Terry Halpin, Object-Role Modeling (ORM/NIAM)

  16. Sample DIAM schemas (Senko, around 1975)

  17. CADIS**:The associative data model based on LEAP (1969)* a <a,r,b> <a,p,c> <r,x,q> etc. p r c x b q w y • "An ALGOL-based Associative Language", J.A. Feldman et al, CACM 12(8):439-449 (Aug. 1969). • ** J.A. Bubenko jr, O.Källhammar, CADIS: Computer Aided Design of Information Systems, in Bubenko, Langefors, Sölvberg (Eds.) Computer-Aided Information Systems Analysis and Design, Studentlitteratur, 1971.

  18. Modelling research issues in the eighties • improving the expressive power of semantic data models 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 & temporal approach

  19. The Deductive Temporal Approach* Picture according To Antoni Olivé ”A Comparison of the Operational and Deductive Approaches to Information Systems Modeling” IFIP Congress, 1986 Part of IB(t) • newart(sofa32, 5, 2500, furniture, t238). • newprice(sofa32, 2700, t419). • delart(sofa32, t726). * Bubenko, around 1977

  20. CS(t) also includes: Derivation rules • qoh(A,Q,T):- article(A,T), • newart(A,Q0,_,_,T0), T0<T, • not (newart(A,_,_,_,T1), T1>T0, T1<T), • findall(S, (sales(A,S,TS), TS>T0, TS<T), SLIST), • findall(L, (delivery(A,L,TL), TL>T0, TL<T), DLIST), • sum(SLIST, SS), • sum(DLIST, DS), • Q is Q0 + DS - SS. Constraints incons(c9,A,T):- newprice(A,P,T), price(A,P2,T-1), P<P2

  21. Multi-temporal Models Proposition: { P(a,b,c,d, …), tv, te, tt} tv = valid time te= event time tt= transaction time

  22. MODELLING IN THE EIGHTIES (cont): - the search for a common framework ISO TC97/SCS/WG3 Concepts and Terminology for the Conceptual Schema and the Information Base, Preliminary Report, 1981 Deductive and multi-temporal models, O-O models, SDM++ IFIP WG 8.1 CRIS: Comparative Review of Information System Design Methodologies conference series CASE-tools, Design and Analysis Assistants, etc Synergy-workshops: PL+AI+DB+SE+IS+.... TC8, WG 8.1: The FRISCO (Framework of Information System Concepts) effort, established 1988. A report was presented in 1996.

  23. Assumes the ANSI/SPARC three-schema approach Ambitions: - to define concepts for conceptual schema languages - to develop a methodology for assessing proposals for conceptual schema languages - to assess candidate proposals for conceptual schema languages - etc. ISO TC97/SCS/WG3Concepts and Terminology for the Conceptual Schema and the Information Base, Preliminary Report, 1981edited by J.J. van Griethuysen et al.

  24. Describing the Universe of Discourse 2 Representation of the abstraction system Abstraction System Conceptual Schema 1 Object System Information Base 3: Representation of the object system Universe of Discourse Description Universe of Discourse 1: Classification, abstraction, generalization, establishing rules, ….

  25. General notions and principles Four ”conceptual schema language candidates” analyzed using an example Universe of Discourse The Entity-Attribute-Relationship approaches The Entity-Relationship approaches The Binary Relationship approaches The Interpreted Predicate Logic approaches ISO TC97/SCS/WG3 Concepts and Terminology for the Conceptual Schema and the Information Base, Preliminary Report, 1981

  26. Ambitions of the eighties: • to better understand 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

  27. On business rules Many business rules are deeply imbeddedin programs of a company’s information system Rule A: If employee x has salary y and if y is greater than z then employee x is also a manager Rule B: All managers work full time Vx,y (employee(x) & salary(x,y) & y > z --> manager(x)) Vx manager(x) --> worksfulltime(x)

  28. Modelling in the nineties:focus onorganisational aspects,participation and understanding … "the understanding and support of i) human activities at all levels in an organisation, ii) change, be it of the product, of the process or of the organisation, and iii) complex user organisations, and individual users" (ESPRIT 91)

  29. The nineties: Widening the scope Interoperable systems Semantic heterogeneity Non-functional requirements Business modelling/engineering Modelling of intentions and actors Participative modelling ”Method knowledge” *) ”Patterns” *) e.g. the EMMSAD (Evaluation of Modelling Methods in Systems Analysis and Design) workshop series, start 1996.

  30. Enterprise Modelling with EKD - integrated descriptions Goals, problems , opportunities, threats, weaknesses, constraints Information concepts (conceptual model) Business rules Business processes (control and flows) Actors and resources Technical IS components and requirements

  31. Sample of an Enterprise Model (EKD) instance

  32. Purpose of modelling: not only IS design Models not only “what” but also “why” Integrates conceptual and process models of the business with objectives, actors, business rules and information system requirements Makes information system solutions traceable to objectives Makes conceptual modelling a “participatory” activity Enterprise Modelling

  33. Iterative development of knowledge and models IS requirements Conceptual Models Objectives Actors Information Concepts Business Rules Processes

  34. Participation in modelling

  35. Modelling during four+ decades Modelling of ”why”, Enterprise models Business rule modelling Information System models • - Extended • scope • Standardisation • efforts Database models Participation and understanding The search for a common framework 2005 Refinement, models and extensions 90-ties Domain Specific ”Ontological Models” and languages Pioneering work - concepts 80-ties User education and participation 70-ties Temporal aspects Formality vs informality 60-ties

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