Exploring Business Strategy Modelling with ArchiMate: A Case Study Approach

Enterprise architecture (EA) is a high-level abstraction of a business’ levels that aids in organizing planning and making better decisions. Researchers have concluded that the scope of EA is not focused only on technology planning but that the lack of business strategy and processes is the most important challenge of EA frameworks. The purpose of this article is to visualize the business strategy of a company using ArchiMate. Having a better understanding of how the concepts of strategic planning are used in businesses, we hope to improve their modelling with ArchiMate. This article adds to the existing literature by evaluating existing EA modelling languages and their skillfulness in model strategy. Furthermore, this article contributes to the identification of challenges in modelling and investigation of the ease of the use of language in the field of strategic planning. Furthermore, this article provides an approach to practitioners and EA architects who are attempting to develop efficient EA modelling projects and solve business complexity problems.

A Robust Mechanism for Categorizing Context-Aware Applications into Generations

The hunt to categorize context-aware applications has been a prevalent issue to developers of context-aware applications. The previous categorizations were based on the functions of the applications. These mechanisms yielded limited results since many applications could not be categorized. This paper categorizes applications into four generations based on developmental trends through a literature survey. The first generation applications focused on data acquisition and used hardware sensors. The second generation applications focused on knowledge acquisition and used software sensors, semantic language and ontology-based modelling languages. The third generation applications focused on intelligent reasoning and used mechanisms to handle information uncertainty. The fourth generation applications deprecate cumbersome ruleset implementations and focus on artificial intelligence whilst taking into consideration the effect of the dynamics of users’ background and preference on contextual information. The study demonstrated that when applications, methods or technologies can be categorized over some time, it is better to classify them into generations.

Developing an Ontology Evaluation Methodology: Cognitive Measure of Quality

<p>Ontologies are formal specifications of shared conceptualizations of a domain. Important applications of ontologies include distributed knowledge based systems, such as the semantic web, and the evaluation of modelling languages, e.g. for business process or conceptual modelling. These applications require formal ontologies of good quality. In this thesis, we present a multi-method ontology evaluation methodology, which consists of two techniques (sentence verification task and recall) based on principles of cognitive psychology, to test how well a specification of a formal ontology corresponds to the ontology users' conceptualization of a domain. Two experiments were conducted, each evaluating the SUMO ontology and WordNet with an experimental technique, as demonstrations of the multi-method evaluation methodology. We also tested the applicability of the two evaluation techniques by conducting a replication study for each. The replication studies obtained findings that point towards the same direction as the original studies, although no significance was achieved. Overall, the evaluation using the multi-method methodology suggests that neither of the two ontologies we examined is a good specification of the conceptualization of the domain. Both the terminology and the structure of the ontologies, may benefit from improvement.</p>

Developing an Ontology Evaluation Methodology: Cognitive Measure of Quality

<p>Ontologies are formal specifications of shared conceptualizations of a domain. Important applications of ontologies include distributed knowledge based systems, such as the semantic web, and the evaluation of modelling languages, e.g. for business process or conceptual modelling. These applications require formal ontologies of good quality. In this thesis, we present a multi-method ontology evaluation methodology, which consists of two techniques (sentence verification task and recall) based on principles of cognitive psychology, to test how well a specification of a formal ontology corresponds to the ontology users' conceptualization of a domain. Two experiments were conducted, each evaluating the SUMO ontology and WordNet with an experimental technique, as demonstrations of the multi-method evaluation methodology. We also tested the applicability of the two evaluation techniques by conducting a replication study for each. The replication studies obtained findings that point towards the same direction as the original studies, although no significance was achieved. Overall, the evaluation using the multi-method methodology suggests that neither of the two ontologies we examined is a good specification of the conceptualization of the domain. Both the terminology and the structure of the ontologies, may benefit from improvement.</p>

Building Normalized Systems from Domain Models in Ecore

Normalized Systems (NS) theory describes how to design and develop evolvable systems. It is applied in practice to generate enterprise information systems using NS Expanders from models of NS Elements. As there are various well-established modelling languages, the possibility to (re-)use them to create NS applications is desired. This paper presents a mapping between the NS metamodel and the Ecore metamodel as a representant of essential structural modelling. The mapping is the basis of the transformation execution tool based on Eclipse Modeling Framework and NS Java libraries. Both the mapping and the tool are demonstrated in a concise case study but cover all essential Ecore constructs. During the work, several interesting similarities of the two metamodels were found and are described, e.g., its meta-circularity or ability to specify data types using references to Java classes. Still, there are significant differences between the metamodels that prevent some constructs from being mapped. The issues with information loss upon the transformation are mitigated by incorporating additional options that serve as key-value annotations. The results are ready to be used for any Ecore models to create an NS model that can be expanded into an NS application.

Language Family Engineering with Product Lines of Multi-level Models

Modelling is an essential activity in software engineering. It typically involves two meta-levels: one includes meta-models that describe modelling languages, and the other contains models built by instantiating those meta-models. Multi-level modelling generalizes this approach by allowing models to span an arbitrary number of meta-levels. A scenario that profits from multi-level modelling is the definition of language families that can be specialized (e.g., for different domains) by successive refinements at subsequent meta-levels, hence promoting language reuse. This enables an open set of variability options given by all possible specializations of the language family. However, multi-level modelling lacks the ability to express closed variability regarding the availability of language primitives or the possibility to opt between alternative primitive realizations. This limits the reuse opportunities of a language family. To improve this situation, we propose a novel combination of product lines with multi-level modelling to cover both open and closed variability. Our proposal is backed by a formal theory that guarantees correctness, enables top-down and bottom-up language variability design, and is implemented atop the MetaDepth multi-level modelling tool.