Modernizing science&engineering software systems

As the demands for modernized legacy systems rise, so does the need for frameworks for information integration and tool interoperability. The Object Management Group (OMG) has adopted the Model Driven Architecture (MDA), which is an evolving conceptual architecture that aligns with this demand. MDA could help solve coupling problems of multidisciplinary character in science and engineering that consist of one or more applications, supported by one or more platforms. The objective of this paper is to describe rigorous techniques to control the evolution from science & engineering software legacy systems to MDA technologies. We propose a rigorous framework to reverse engineering code in the context of MDA. Considering that validation, verification and consistency are crucial activities in the modernization of systems that are critical to safety, security and economic profits, our approach emphasizes the integration of MDA with formal methods

A Metamodeling Approach to Teaching Conceptual Modeling at Large

In the authors\u27 university there is a challenge, with respect to Conceptual Modeling topics, of bridging the gap between bachelor-level studies and research work. At bachelor-level, Conceptual Modeling is subordinated to Software Engineering topics consequently making extensive use of software design standards. However, at doctoral level or in project-based work, modeling methods must be scientifically framed within wider-scoped paradigms - Design Science, Enterprise Modeling etc. In order to bridge this gap, we developed a teaching artifact to present Conceptual Modeling as a standalone discipline that can produce its own artifacts, driven by requirements in a variety of domains. The teaching artifact is an agile modeling method that is iteratively implemented by students. The key takeaway revelation for students is that a modeling language is a knowledge schema that can be tailored and migrated for specific purposes just like a database schema, to accommodate an application domain and its modeling requirements

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

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\u27s 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

Semantic Bridging between Conceptual Modeling Standards and Agile Software Projects Conceptualizations

Software engineering benefitted from modeling standards (e.g. UML, BPMN), but Agile Software Project Management tends to marginalize most forms of documentation including diagrammatic modeling, focusing instead on the tracking of a project\u27s backlog and related issues. Limited means are available for annotating Jira items with diagrams, however not on a granular and semantically traceable level. Business processes tend to get lost on the way between process analysis (if any) and backlog items; UML design decisions are often disconnected from the issue tracking environment. This paper proposes domain-specific conceptual modeling to obtain a diagrammatic view on a Jira project, motivated by past conceptualizations of the agile paradigm while also offering basic interoperability with Jira to switch between environments and views. The underlying conceptualization extends conceptual modeling languages (BPMN, UML) with an agile project management perspective to enrich contextual traceability of a project\u27s elements while ensuring that data structures handled by Jira can be captured and exposed to Jira if needed. Therefore, concepts underlying the typical software development project management are integrated with established modeling concepts and tailored (with metamodeling means) for the domain-specificity of agile project management. A Design Science approach was pursued to develop a modeling method artifact, resulting in a domain-specific modeling tool for software project managers that want to augment agile practices and enrich issue annotation

Model Continuity in Discrete Event Simulation: A Framework for Model-Driven Development of Simulation Models.

Most of the well known modeling and simulation methodologies state the importance of conceptual modeling in simulation studies and they suggest the use of conceptual models during the simulation model development process. However, only a limited number of methodologies refers to howto move from a conceptual model to an executable simulation model. Besides, existing modeling and simulation methodologies do not typically provide a formal method for model transformations between the models in different stages of the development process. Hence, in the current M&S practice, model continuity is usually not fulfilled. In this article, a model driven development framework for modeling and simulation is in order to bridge the gap between different stages of a simulation study and to obtain model continuity. The applicability of the framework is illustrated with a prototype modeling environment and a case study in the discrete event simulation domain

Design Approach to Unified Service API Modeling for Semantic Interoperability of Cross-enterprise Vehicle Applications

This work was partially supported by Ministry of Education, Youth and Sports of the Czech Republic, university specific research, project SGS-2019-018 Processing of heterogeneous data and its specialized applications

A Proposal for Deploying Hybrid Knowledge Bases: the ADOxx-to-GraphDB Interoperability Case

Graph Database Management Systems brought data model abstractions closer to how humans are used to handle knowledge - i.e., driven by inferences across complex relationship networks rather than by encapsulating tuples under rigid schemata. Another discipline that commonly employs graph-like structures is diagrammatic Conceptual Modeling, where intuitive, graphical means of explicating knowledge are systematically studied and formalized. Considering the common ground of graph databases, the paper proposes an integration of OWL ontologies with diagrammatic representations as enabled by the ADOxx metamodeling platform. The proposal is based on the RDF-semantics variant of OWL and leads to a particular type of hybrid knowledge bases hosted, for proof-of-concept purposes, by the GraphDB system due to its inferencing capabilities. The approach aims for complementarity and integration, providing agile diagrammatic means of creating semantic networks that are amenable to ontology-based reasoning