Model Abstraction, Complexity Management in Conceptual Modeling, Ontology-
Based Conceptual Modeling, Conceptual Model Modularization, Ontological Views, On-
toUML
Name: GUYLERME VELASCO DE SOUZA FIGUEIREDO
Publication date: 16/09/2022
Examining board:
Name |
Role |
|---|---|
| GIANCARLO GUIZZARDI | Advisor |
Summary: Reference conceptual models are used to capture complex and critical domain information.
However, as the complexity of a domain grows, so does the size and complexity of the
model that represents it. Over the years, different complexity management techniques
in large-scale conceptual models have been developed to extract value from models that,
due to their size, are challenging to understand. These techniques, however, run into
some limitations, such as the possibility of execution without human interaction, semantic
cohesion of modules/views generated from the model, and generating an abstracted version
of the model so that it can present the essential elements of the domain, among others. This
thesis proposes two algorithms to facilitate the understanding of large-scale conceptual
models by tackling the problem from two different angles. The first consists in extracting
smaller self-contained modules from the original model. The second consists in abstracting
the original model, thereby providing a summarized view of the main elements and how
they relate to each other in the domain. Both algorithms we propose in this thesis require
no input from modelers, are deterministic, and computationally inexpensive. To evaluate
the abstraction algorithm for conceptual models, we carried out an empirical research
aimed at a comparative analysis taking into account other competing approaches.
