JB June 2005 A Historical Perspective on Conceptual Modelling: from Information Algebra to Enterprise Modelling and Ontologies Janis A. Bubenko jr Royal Institute of Technology, Stockholm, Sweden

JB June 2005 Young and Kent (1958) “Abstract Formulation of Data Processing Problems” Information set/item Defining relationship Producing relationship Conditions Temporal aspects

JB June 2005 Why the need for an abstract formalism? 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 *). *) Young and Kent, Journal of Industrial Engineering, Nov. – Dec. 1958, pp

JB June 2005 Why Conceptual Modelling in Information 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.

JB June 2005 Modelling during four decades Pioneering work - concepts Refinement, models and extensions The search for a common framework Participation and understanding 60-ties 70-ties 80-ties 90-ties

JB June 2005 Pioneers in IS modelling: 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

JB June 2005 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

JB June 2005 Information Algebra, basic concepts 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, …. 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.

JB June 2005 The Scandinavian School: Langefors e = s system point a attribute v value t time e = s system point a attribute v value t time Langefors, 1963 * the infological and the datalogical realms * the “elementary message” * the “elementary file”

JB June 2005 Langefors 1966

JB June 2005 Langefors 1966 (cont)

JB June 2005 THE PERIOD: ”REFINEMENT AND EXTENSIONS" 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)

JB June 2005 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,...

JB June 2005 Jean-Raymond Abrial: ”Data Semantics” (1974) Influenced by: GDBMS, Codd’s Relational Model, AI-techniques, … Binary model sexp person spouse/ spouse children/parents sex/personofsex age/personofage R4=rel(person, person, parents = afn(2,2), children = afn(0, ∞)) -Schema: fact types, rules -Rules: constraints, derivation rules -Internal vs external names number

JB June 2005 A sample NIAM schema (Nijssen) * Source: Terry Halpin, Object-Role Modeling (ORM/NIAM) *

JB June 2005 Sample DIAM schemas (Senko, around 1975)

JB June 2005 CADIS**:The associative data model based on LEAP (1969)* a b c r p q x y w etc. "An ALGOL-based Associative Language", J.A. Feldman et al, CACM 12(8): (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.

JB June 2005 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

JB June 2005 The Deductive Temporal Approach* newart(sofa32, 5, 2500, furniture, t238). newprice(sofa32, 2700, t419). delart(sofa32, t726). Part of IB(t) Picture according To Antoni Olivé ”A Comparison of the Operational and Deductive Approaches to Information Systems Modeling” IFIP Congress, 1986 * Bubenko, around 1977

JB June 2005 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 CS(t) also includes:

JB June 2005 Multi-temporal Models Proposition: { P(a,b,c,d, …), t v, t e, t t } t v = valid time t e = event time t t = transaction time

JB June 2005 MODELLING IN THE EIGHTIES (cont): 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 A report was presented in the search for a common framework

JB June 2005 ISO TC97/SCS/WG3 Concepts and Terminology for the Conceptual Schema and the Information Base, Preliminary Report, 1981 edited by J.J. van Griethuysen et al. 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.

JB June 2005 Describing the Universe of Discourse Universe of Discourse Universe of Discourse Description 1 2 Representation of the abstraction system 3: Representation of the object system Abstraction System Object System Conceptual Schema Information Base 1: Classification, abstraction, generalization, establishing rules, ….

JB June 2005 ISO TC97/SCS/WG3 Concepts and Terminology for the Conceptual Schema and the Information Base, Preliminary Report, 1981  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

JB June 2005 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

JB June 2005 On business rules Many business rules are deeply imbedded in 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 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) Vx,y (employee(x) & salary(x,y) & y > z --> manager(x)) Vx manager(x) --> worksfulltime(x)

JB June 2005 Modelling in the nineties: focus on organisational 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)

JB June 2005 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.

JB June 2005 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

JB June 2005 Sample of an Enterprise Model (EKD) instance

JB June 2005 Enterprise Modelling 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

JB June 2005 Iterative development of knowledge and models Objectives Information Concepts Processes Actors IS requirements Business Rules Conceptual Models

JB June 2005 Participation in modelling

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