Henderik Alex Proper

Formal definition of a conceptual language for the description and manipulation of information models

Abstract Conceptual data modelling techniques aim at the representation of data at a high level of abstraction. This implies that conceptual data modelling techniques should not only be capable of naturally representing complex structures, but also the rules (constraints) that must hold for these structures. Contemporary data modelling techniques, however, do not provide a language, which on the one hand has a formal semantics and on the other hand leads to natural looking expressions, for formulating these constraints. In this paper such a language is defined for an existing data modelling technique (PSM), which is a generalisation of object-role models (such as ER or NIAM). In this language not only constraints, but also queries and updates can be expressed on a conceptual level.

A fundamental view on the process of conceptual modeling

In an ongoing effort to better understand the process of creating conceptual models (in particular formal ones), we present a fundamental view of the process of modeling. We base this view on the idea that participants in such a process are involved in a deliberate and goal-driven effort to share and reconcile representations of their personal conceptions of (parts of) the world. This effort takes the shape of a modeling dialogue, involving the use of controlled language. We thus take a fundamental approach to subjective aspects of modeling, as opposed to traditional approaches which essentially consider models as objective entities. We position and present our initial theory of modeling, and briefly discuss how we intend to validate and further develop it.

EA Anamnesis: An Approach for Decision Making Analysis in Enterprise Architecture

Enterprise Architecture (EA) modeling languages can express the business-to-IT-stack for an organization, showing how changes in the IT landscape impact business aspects and vice versa. Yet EA languages provide only the final architectural design, not the rationale behind this design. In earlier work, the authors presented the EA Anamnesis approach for EA rationalization. The authors discussed how EA Anamnesis forms a complement to current EA modeling languages, showing for example design alternatives, EA artifact selection criteria and the decision making strategy that was used. In this paper, the authors extend EA Anamnesis with a capability for organizational learning. In particular, the authors present an integration of two viewpoints presented in earlier work: (1) an ex-ante decision making viewpoint for rationalizing EA during decision making, which for example captures a decision and its anticipated consequences, and (2) an ex-post decision making viewpoint, which for example captures the unanticipated decision consequences, and possible adjustments in criteria. The authors use a fictitious, yet realistic, case study to illustrate our approach.

Bridging value modelling to ArchiMate via transaction modelling

The ArchiMate modelling language provides a coherent and a holistic view of an enterprise in terms of its products, services, business processes, actors, business units, software applications and more. Yet, ArchiMate currently lacks (1) expressivity in modelling an enterprise from a value exchange perspective, and (2) rigour and guidelines in modelling business processes that realize the transactions relevant from a value perspective. To address these issues, we show how to connect e

Enterprise Coherence in the Dutch Ministry of Social Affairs and Employment

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Giving meaning to enterprise architectures: architecture principles with ORM and ORC

value, a technique for value modelling, to ArchiMate via transaction patterns from the DEMO methodology. Using ontology alignment techniques, we show a transformation between the meta models underlying e

The Extended Enterprise Coherence-Governance Assessment

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