Information Modeling : The process and the required competencies of its participants

1. Information Modeling: The process and the required competencies of its participants Paul Frederiks Theo van der Weide

2. Position within Archimate • The ArchiMate project is a research initiative that provides concepts and techniques to support an architect in the visualization, communication and analysis of integrated architectures. • In this paper focus on: communication and analysis.

3. Requirements Engineering • Discovering the purpose for which software is meant • Identification stakeholders and their needs • Documentation stages: • Analysis, • Communication, • Negotiation • Decision making • Subsequent implementation • Closing gap informal - formal

4. Information modeling • Identify involved information objects • Resulting model used as base for communication and understanding • Relying on common base for understanding • For example: (semi-)natural language

5. Motivation Domain expert System analyst

6. Informal semantic function Dialogue document Formal semantic function The information modeling process

7. The goal • Find a minimal (information) grammar capable to generate/accept the sentences of the informal specification • Minimal in the sense that each formal concept is motivated form the informal specification.

8. Correct model • Conceptual model as generative device • Correctness: • Completeness principle: with respect to Universe of Discourse • Falsification principle: with respect to informal specification

9. Responsibilities System analyst: Falsification Domain expert: Completeness

10. Effectiveness How well accomplish participants their share • How well can domain expert • provide a domain description • validate paraphrased description • How well can system analyst • map sentences onto modeling concepts • evaluate a validation Number of cycles?

11. A theory for Information Modeling • Our goal: try to find a theory for information modeling • Main theorem for Information ModelingThe probability of a model being incorrect, as a function of the dialogue length, tends to zero for a combination of qualified domain expert and system analyst.

12. Refined

13. Refinement elicitation phase • Collecting significant objects • D1: DE can provide complete set of information objects • A1: SA can handle implicit knowledge • Verbalization • D2: DE can provide any number of describing sample sentences • A1: SA can handle implicit knowledge

14. Refinement elicitation phase • Reformulation: • D3: DE can split into elementary sentences • D4: DE can reformulate in unifying format • D5: DE can order sentences according dynamics in application domain • A2: SA can validate sentences for consistency

15. Refinement modeling phase • Grammatical analysis and abstraction: • A3: SA can perform grammatical analysis • A4: SA can abstract sentence structure, and match these structures onto modeling concepts

16. Refinement validation phase • Production: • A5: SA can match abstract sentence structure with concepts • A6: SA can generate new sample sentences • Feed back: • D6: DE can validate description • D7: DE can judge significance of sample sentence • A2: SA can validate sentencesfor consistency

17. Verification phase • Verification: • A7: SA can think on an abstract level

18. Summary

19. Conclusion • Having these competencies at a sufficient level: • DE will eventually be complete • SA will guide DE in being complete • Thus: information modeling will lead eventually to a correct model

20. Domain expert D1: completeness D2: describing D3: splitting D4: normalization D5: ordering D6: validation D7: significance System analyst A1: implicit knowledge A2: consistency A3: grammatical analysis A4: modeling A5: concretizing A6: generation A7: fundamental Base skills

21. Controlling natural language (1) • Completeness: • D1: providing complete set of information objects • D2: providing any number of significant sample sentences • A1: handling implicit knowledge • A6: generating sample sentences • Verbosity: • D3: splitting sentences • D4: reformulating in unifying format • D5: ordering sample sentences • D7: judging significance • A3: recognizing similarity • A4: abstracting sentence structures

22. Controlling natural language (2) • Ambiguity: • D2: providing any number of significant sample sentences • D6: validating description application domain • A2: validating sample sentences for consistency • A6: generating sample sentences • Consistency: • D2: providing any number of significant sample sentences • D6: validating description application domain • D7: judging significance • A2: validating sample sentences for consistency • A6: generating sample sentences

23. Controlling natural language (3) • Mixed level of abstraction: • D6: validating description application domain • A3: recognizing similarity • A4: abstracting sentence structures • A5: matching natural language with modeling concepts

24. Future research • Introduction of open modeling concepts • Extension of the dialog model

25. Open modeling concepts • Natural language may be seen as a basis • Other media might be more effective: a language with informal symbols and rules • Solution: allow open modeling concepts. • “Empowering a weak formalism by negotiation”, in preparation

26. Extending the dialog • In practice many stakeholders • particular view • goals • The chatbox model • Dialog involves several participants • Sentence oriented • Subdialogs are possible

27. Thank you, Questions?