Systematic analysis and evaluation of visual conceptual modeling language notations

Bork, Dominik; Karagiannis, Dimitris; Pittl, Benedikt

doi:10.1109/rcis.2018.8406652

Cited by 15 publications

(11 citation statements)

References 20 publications

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“…• Domain expert: the source of domain knowledge that must be acquired and embedded in all building blocks of a modeling method (i.e., in notational specificity (see Bork, Karagiannis, & Pittl, 2018a), in concept definitions, in functionality reflecting some aspects of business logic).…”

Section: Figure 1 the Key Omilab Motivator: The Co-creation Of Modelmentioning

confidence: 99%

“…Finally, the OMiLAB aims to establish valuable training resources for tool developers and digital product engineers in order to facilitate productive prototyping based on scientific principles, such as how to design meaningful and intuitive graphical visualizations (see Moody, 2009;Stark, Braun, & Esswein, 2017;Bork et al, 2018a), how to specify metamodels (Bork, Karagiannis, & Pittl, 2018b), or how to specify mechanisms & algorithms that process the model semantics . microservice architecture, each OMiLAB project can disseminate information about the domain requirements, use cases, tutorials, training materials, a wiki, and download pages for its prototypes (e.g., modeling tools) in a structured way.…”

Section: Omilab In Educationmentioning

confidence: 99%

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An Open Platform for Modeling Method Conceptualization: The OMiLAB Digital Ecosystem

Bork¹,

Buchmann²,

Karagiannis³

et al. 2019

CAIS

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This paper motivates, describes, demonstrates in use, and evaluates the Open Models Laboratory (OMiLAB)-an open digital ecosystem designed to help one conceptualize and operationalize conceptual modeling methods. The OMiLAB ecosystem, which a generalized understanding of "model value" motivates, targets research and education stakeholders who fulfill various roles in a modeling method's lifecycle. While we have many reports on novel modeling methods and tools for various domains, we lack knowledge on conceptualizing such methods via a full-fledged dedicated open ecosystem and a methodology that facilitates entry points for novices and an open innovation space for experienced stakeholders. This gap continues due to the lack of an open process and platform for 1) conducting research in the field of modeling method design, 2) developing agile modeling tools and model-driven digital products, and 3) experimenting with and disseminating such methods and related prototypes. OMiLAB incorporates principles, practices, procedures, tools, and services required to address the issues above since it focuses on being the operational deployment for a conceptualization and operationalization process built on several pillars: 1) a granularly defined "modeling method" concept whose building blocks one can customize for the domain of choice, 2) an "agile modeling method engineering" framework that helps one quickly prototype modeling tools, 3) a model-aware "digital product design lab", and 4) dissemination channels for reaching a global community. In this paper, we demonstrate and evaluate the OMiLAB in research with two selected application cases for domain-and case-specific requirements. Besides these exemplary cases, OMiLAB has proven to effectively satisfy requirements that almost 50 modeling methods raise and, thus, to support researchers in designing novel modeling methods, developing tools, and disseminating outcomes. We also measured OMiLAB's educational impact.

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Section: Figure 1 the Key Omilab Motivator: The Co-creation Of Modelmentioning

confidence: 99%

Section: Omilab In Educationmentioning

confidence: 99%

An Open Platform for Modeling Method Conceptualization: The OMiLAB Digital Ecosystem

Bork¹,

Buchmann²,

Karagiannis³

et al. 2019

CAIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…A lot of research can be found on the analysis of models, focusing for example on the usage of modeling concepts by modelers [33], the evaluation of modeling languages according to their notation [37,32,7], their semantics [42,19], their ontological completeness [18], their metamodels [34], their specification techniques [6], or their applicability in certain use cases [20] and domains [24,25]. These approaches however never investigate the syntactic metamodel backbone of the modeling language and the way metamodels are structured.…”

Section: Related Workmentioning

confidence: 99%

“…[23]). A lot of research is focusing on the evaluation of modeling methods from a semantical point of view [19,18], from a notational point of view [37,41,7], or on methodological guidance in developing modeling languages [16,27] and methods [36,3,13]. By contrast, only limited research focuses on metamodels and their design.…”

Section: Introductionmentioning

confidence: 99%

Metamodel-Based Analysis of Domain-Specific Conceptual Modeling Methods

Bork

2018

Lecture Notes in Business Information Processing

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Metamodels play a pivotal role in conceptual modeling as they manifest the abstraction level applied when creating conceptual models. Consequently, design decisions made by the metamodel developer determine utility, capabilities, and expressiveness of the conceptual modeling language-and eventually the created models. However, only limited research defines and applies metrics for analyzing the structure and capabilities of a metamodel, and eventually support the development of new metamodels. This not only concerns general-purpose modeling languages, but also domain-specific ones, which usually undergo shorter update cycles. The paper at hand introduces a generic analysis framework to syntactically analyze modeling languages. The framework is applied to 40 metamodels of domain-specific conceptual modeling languages (DSML). This research establishes a foundation to support metamodel development in the future. The contribution of this paper is threefold: i) an analysis framework for conceptual modeling method metamodels is proposed, ii) results from applying this framework to 40 ADOxx-based DSML metamodels are presented, and iii) a human-based reasoning after comparison of these results with Ecore-based metamodels is conducted.

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“…In the context of overcoming the notation design challenge, the demand for intuitively understandable graphical notations advances [23], consequently asking to fill a research gap of specialized design techniques [16,22]. This affects both general-purpose (see [5,13]) and domain-specific modeling languages (DSMLs) [31].…”

Section: Introductionmentioning

confidence: 99%

A technique for evaluating and improving the semantic transparency of modeling language notations

Bork

Roelens

2021

Softw Syst Model

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The notation of a modeling language is of paramount importance for its efficient use and the correct comprehension of created models. A graphical notation, especially for domain-specific modeling languages, should therefore be aligned to the knowledge, beliefs, and expectations of the targeted model users. One quality attributed to notations is their semantic transparency, indicating the extent to which a notation intuitively suggests its meaning to untrained users. Method engineers should thus aim at semantic transparency for realizing intuitively understandable notations. However, notation design is often treated poorly—if at all—in method engineering methodologies. This paper proposes a technique that, based on iterative evaluation and improvement tasks, steers the notation toward semantic transparency. The approach can be efficiently applied to arbitrary modeling languages and allows easy integration into existing modeling language engineering methodologies. We show the feasibility of the technique by reporting on two cycles of Action Design Research including the evaluation and improvement of the semantic transparency of the Process-Goal Alignment modeling language notation. An empirical evaluation comparing the new notation against the initial one shows the effectiveness of the technique.

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Systematic analysis and evaluation of visual conceptual modeling language notations

Cited by 15 publications

References 20 publications

An Open Platform for Modeling Method Conceptualization: The OMiLAB Digital Ecosystem

An Open Platform for Modeling Method Conceptualization: The OMiLAB Digital Ecosystem

Metamodel-Based Analysis of Domain-Specific Conceptual Modeling Methods

A technique for evaluating and improving the semantic transparency of modeling language notations

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