information modeling: the process and the required competencies of its participants paul frederiks...

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

Paul FrederiksTheo van der Weide

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.


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


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


Motivation

Domain expert

System analyst


The information modeling process

Formalsemanticfunction

Informalsemanticfunction

Dialoguedocument


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.


Correct model

• Conceptual model as generative device

• Correctness:– Completeness principle: with respect

to Universe of Discourse– Falsification principle: with respect to

informal specification


Responsibilities

Domain expert:

Completeness

System analyst:

Falsification


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?


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.


Refined


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


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


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


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


Verification phase

• Verification:– A7: SA can think on an

abstract level


Summary


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


Base skills

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


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



• 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



• Mixed level of abstraction:– D6: validating description application domain– A3: recognizing similarity– A4: abstracting sentence structures– A5: matching natural language with modeling

concepts


Future research

• Introduction of open modeling concepts

• Extension of the dialog model


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


Extending the dialog

• In practice many stakeholders– particular view– goals

• The chatbox model– Dialog involves several participants– Sentence oriented– Subdialogs are possible


Thank you,

Questions?

information modeling: the process and the required competencies of its participants paul frederiks...

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