Mitigating response distortion in IS ethics research

Distributed construction of conceptual models may lead to a set of problems when these models are to be compared or integrated. Different kinds of comparison conflicts are known (e.g. naming conflicts or structural conflicts), the resolution of which is subject of different approaches. However, the expost resolution of naming conflicts raises subsequent problems that origin from semantic diversities of namings – even if they are syntactically the same. Therefore, we propose an approach that allows for avoiding naming conflicts in conceptual models already during modelling. This way, the ex-post resolution of naming conflicts becomes obsolete. In order to realise this approach we combine domain thesauri as lexical conventions for the use of terms, and linguistic grammars as conventions for valid phrase structures. The approach is generic in order to make it reusable for any conceptual modelling language

Unified Enterprise Knowledge Representation with Conceptual Models - Capturing Corporate Language in Naming Conventions

Conceptual modeling is an established instrument in the knowledge engineering process. However, a precondition for the usability of conceptual models is not only their syntactic correctness but also their semantic comparability. Assuring comparability is quite challenging especially when models are developed by different persons. Empirical studies show that such models can vary heavily, especially in model element naming, even if they are meant to express the same issue. In contrast to most ontology-driven approaches proposing the resolution of these differences ex-post, we introduce an approach that avoids naming differences in conceptual models already during modeling. Therefore we formalize naming conventions combining domain thesauri and phrase structures based on a linguistic grammar. This allows for guiding modelers automatically during the modeling process using standardized labels for model elements, thus assuring unified enterprise knowledge representation. Our approach is generic, making it applicable for any modeling language

Using ontologies to synchronize change in relational database systems

Ontology is a building block of the semantic Web. Ontology building requires a detailed domain analysis, which in turn requires financial resources, intensive domain knowledge and time. Domain models in industry are frequently stored as relational database schemas in relational databases. An ontology base underlying such schemas can represent concepts and relationships that are present in the domain of discourse. However, with ever increasing demand for wider access and domain coverage, public databases are not static and their schemas evolve over time. Ontologies generated according to these databases have to change to reflect the new situation. Once a database schema is changed, these changes in the schema should also be incorporated in any ontology generated from the database. It is not possible to generate a fresh version of the ontology using the new database schema because the ontology itself may have undergone changes that need to be preserved. To tackle this problem, this paper presents a generic framework that will help to generate and synchronize ontologies with existing data sources. In particular we address the translation between ontologies and database schemas, but our proposal is also sufficiently generic to be used to generate and maintain ontologies based on XML and object oriented databases

Ontological Evaluation of Conceptual Models

The objective of this paper is to present a philosophically sound approach to conceptual model evaluation. Accordingly, the ontological evaluation of conceptual models is enriched with a linguistic interpretivist perspective. The need for such an approach to evaluation is justified by the substantial economic importance of conceptual models. The quality of a conceptual model has a significant impact on other IT artefacts and, thus, on the costs of IT projects. However, little research has so far focused on their evaluation. In the course of this paper, we develop a framework which describes the current state of research and recognizes neglected research fields. With the aid of this framework we identify a notable shortcoming in conceptual model evaluation research, especially with respect to philosophically sound evaluation procedures. Based on these findings we address the following research questions: What are the shortcomings in current evaluation research, what are the merits of ‘ontological evaluation’ in this context, and how can the linguistic interpretivist approach help to form a comprehensive and philosophically sound conceptual model evaluation approach

Configuration management for models : generic methods for model comparison and model co-evolution

It is an undeniable fact that software plays an important role in our lives. We use the software to play our music, to check our e-mail, or even to help us drive our car. Thus, the quality of software directly influences the quality of our lives. However, the traditional Software Engineering paradigm is not able to cope with the increasing demands in quantity and quality of produced software. Thus, a new paradigm of Model Driven Software Engineering (MDSE) is quickly gaining ground. MDSE promises to solve some of the problems of traditional Software Engineering (SE) by raising the level of abstraction. Thus, MDSE proposes the use of models and model transformations, instead of textual program files used in traditional SE, as means of producing software. The models are usually graph-based, and are built by using graphical notations – i.e. the models are represented diagrammatically. The advantages of using graphical models over text files are numerous, for example it is usually easier to deduce the relations between different model elements in their diagrammatic form, thus reducing the possibility of defects during the production of the software. Furthermore, formal model transformations can be used to produce different kinds of artifacts from models in all stages of software production. For example, artifacts that can be used as input for model checkers or simulation tools can be produced. This enables the checking or simulation of software products in the early phases of development, which further reduces the probability of defects in the final software product. However, methods and techniques to support MDSE are still not mature enough. In particular methods and techniques for model configuration management (MCM) are still in development, and no generic MCM system exists. In this thesis, I describe my research which was focused on developing methods and techniques to support generic model configuration management. In particular, during my research, I focused on developing methods and techniques for supporting model evolution and model co-evolution. Described methods and techniques are generic and are suitable for a state-based approach to model configuration management. In order to support the model evolution, I developed methods for the representation, calculation, and visualization of state-based model differences. Unlike in previously published research, where these three aspects of model differences are dealt with in separation, in my research all these three aspects are integrated. Thus, the result of model differences calculation algorithm is in the format which is described by my research on model differences representation. The same representation format of model differences is used as a basis of my approach to differences visualization. It is important to notice that the developed representation format for model differences is metamodel independent, and thus is generic, i.e., it can be used to represent differences between all graph-based models. Model co-evolution is a term that describes the problem of adapting models when their metamodels evolve. My solution to this problem has three steps. In the first step a special metamodel MMfMM is introduced. Unlike in traditional approaches, where metamodels are represented as instances of a metametamodel, in my approach the metamodels are represented by models which are instances of an MMfMM. In the second step, since metamodels are represented by models, previously defined methods and techniques for model evolution are reused to represent and calculate the metamodel differences. In the final step I define an algorithm that uses the calculated metamodel differences to adapt models conforming to the evolved metamodel. In order to validate my approaches to model evolution and model co-evolution, I have developed a tool for comparing models and visualizing resulting differences, and a tool for model co-evolution. Moreover, I have developed a method to compare tools for model comparison, and using this method I have conducted a series of experiments in which I compared the tool I developed to an industrial tool called EMFCompare. The results of these experiments are also presented in the thesis. Furthermore, in order to validate my tool and approach to model co-evolution, I have also specified and conducted several experiments. The results of these experiments are also presented in the thesis