Towards an Investigation of the Conceptual Landscape of Enterprise Architecture

Lecture Notes in Business Information Processing

Hoppenbrouwers

2012

Part 4: Enterprise Architecture and Enterprise ModelingInternational audienceIn this paper we discuss the use and challenges of identifying communities with shared semantics in Enterprise Modeling. People tend to understand modeling meta-concepts (i.e., a modeling language’s constructs or types) in a certain way and can be grouped by this understanding. Having an insight into the typical communities and their composition (e.g., what kind of people constitute a semantic community) would make it easier to predict how a conceptual modeler with a certain background will generally understand the meta-concepts he uses, which is useful for e.g., validating model semantics and improving the efficiency of the modeling process itself. We demonstrate the use of psychometric data from two studies involving experienced (enterprise) modeling practitioners and computing science students to find such communities, discuss the challenge that arises in finding common real-world factors shared between their members to identify them by and conclude that the common (often implicit) grouping properties such as similar background, focus and modeling language are not supported by empirical data

Section: Methods and Used Data Samplesmentioning

confidence: 99%

Challenges of Identifying Communities with Shared Semantics in Enterprise Modeling

Lecture Notes in Business Information Processing

Hoppenbrouwers

2012

“…The studies reported on in this article investigate the understanding participants have for the concepts actors, events, goals, processes, resources, restrictions and results in the context of (conceptual) modeling. These (meta) concepts were derived from an earlier performed analysis, as reported in (van der Linden et al, 2011). This analysis was specifically focused on finding the common high-level meta-concepts shared between the specifications of a number of languages and methods covering different aspects used in enterprise modeling (e.g., processes, value exchanges, goals, architecture, performance, security).…”

Section: Our Study Setupmentioning

confidence: 99%

On the Identification of Modeler Communities

International Journal of Information System Modeling and Design

Hoppenbrouwers

Proper

2014

The authors discuss the use and challenges of identifying communities with shared semantics in Enterprise Modeling (EM). People tend to understand modeling meta-concepts (i.e., a modeling language's constructs or types) in a certain way and can be grouped by this conceptual understanding. Having an insight into the typical communities and their composition (e.g., what kind of people constitute such a semantic community) can make it easier to predict how a conceptual modeler with a certain background will generally understand the meta-concepts s/he uses, which is useful for e.g., validating model semantics and improving the efficiency of the modeling process itself. The authors have observed that in practice decisions to group people based on certain shared properties are often made, but are rarely backed up by empirical data demonstrating their supposed efficacy. The authors demonstrate the use of psychometric data from two studies involving experienced (enterprise) modeling practitioners and computing science students to find such communities. The authors also discuss the challenge that arises in finding common real-world factors shared between their members to identify them by and conclude that there is no empirical support for commonly used (and often implicit) grouping properties such as similar background, focus and modeling language.

“…The concepts we selected (ACTOR, EVENT, GOAL, PROCESS, RESOURCE, RESTRICTION, RESULT) derived from previous work into the analysis of modeling languages used for (different aspects modeled in) enterprise modeling (see [21]). We used descriptions of the concepts that were wide enough to not prime participants on a particular interpretation.…”

Section: A Materialsmentioning

confidence: 99%

On the Semantic Feature Structure of Modeling Concepts: An Empirical Study

2014 IEEE 16th Conference on Business Informatics

Zee

2014

Abstract-Is an actor typically considered a human being? What about an autonomous entity? We investigate the typical feature structure of common modeling concepts in order to create an empirically grounded description of the semantic feature structure that people implicitly use while reasoning about, and with such concepts. Apart from the insights into modeling concept structure that this work presents, consequences for the quality of models and use of modeling languages are discussed. We finally discuss in more detail how the process of modeling, especially when it involves multiple people with different backgrounds, modeling different aspects (i.e., enterprise modeling), stands to benefit from more insights into how the individual modelers see the basic modeling concepts shared between them.