Modeling is an important part of the lifecycle of systems, starting from the early design stages. Modeling is also very useful in the process of studying an unfamiliar, existing system. Conceptual modeling methodologies disregard certain aspects of the system, making modeling or understanding a model a simpler task as they convey the important aspects of a system in an effective way.
One of the shortcomings of conceptual modeling methodologies is the simplification of the system being modeled at the expense of suppressing computational aspects. This research presents two approaches for solving this computational simplification problem for conceptual models that use Object Process Methodology (OPM), an emerging ISO 19450 standard modeling methodology.
OPM offers a holistic approach for modeling systems that combines the structure and behavior of the system in a single diagram type. We expand the quantitative aspects of an OPM model by representing complex quantitative behavior using alternative approaches that employ MATLAB or Simulink without compromising the holism and simplicity of the OPM conceptual model. The first approach, AUTOMATLAB, expands the OPM model to a fullfledged MATLAB-based simulation. The second, OPM Computational Subcontractor approach, replaces low-level processes of the OPM model with computation-enhanced MATLAB functions or Simulink models.
We demonstrated the two approaches with MATLAB and Simulink enhanced OPM models of a biological system and a radar system, respectively. An evaluation the AUTOMATLAB approach, which compared system modeling and analysis with and without the AUTOMATLAB layer has indicated several benefits of the additional AUTOMATLAB layer compared to a non-enhanced OPM model.
Model-based engineering approaches are increasingly adopted for various systems engineering tasks. Leading characteristics of modeling languages include clarity, expressiveness and comprehension. Exact semantics of the modeling language used in a model-based framework is critical for a successful system development process. As some of the characteristics contradict each other, designing a “good” modeling language is a complex task. Still, an important precondition for acceptance of a modeling language is that its semantics must be precisely and formally defined.
Object Process Methodology (OPM) is a holistic, integrated model-based approach to systems development. The applicability of the OPM modeling language was studied through modeling of many complex systems from disparate domains, including business processing, real-time systems architecture, web applications development and mobile agents design. Experience with OPM has underlined the need to enrich the language with new constructs. An adverse side effect of the increased OPM expressiveness was that it also became more complex and in some cases ambiguous or undefined.
In this work, we define operational semantics for the core of the OPM language using a clocked transition system (CTS) formalism. The operational semantics consists of an execution framework and a set of transition rules. The principles and rules underlying this framework provide for determining the timing of transitions to be taken in a system modeled in OPM. The set of transition rules, adjusted to the OPM rules, describe all the possible changes in the system state based on the current state of the system and the set of its inputs.
Similar works defining formal operational semantics include Statecharts by David Harel, formalizing UML Statecharts with combined graph-grammar and model transition system (MTS) by Varro et al., and formalizing activ1ity diagrams for workflow models by translating the subject model into a format appropriate for a model checker.
Well-defined operational semantics enables extending OPM with a wide range of testing tools, including model-based simulation, execution and verification, which can employ the theoretical executable framework developed in this work.
As a solid proof of concept, we have developed an OPM-to-SMV (Symbolic Model Verification) translation tool for models in the domain of Molecular Biology (MB), based on the OPM-CTS framework principles and a subset of the transition rules. Using this tool, a holistic OPM model describing both research hypothesis and facts from state-of-the-art MB papers can be translated into an SMV verification tool. The generated SMV model can be verified against specifications, based on information found in MB research papers and manually inserted into the SMV tool. The verification process helps to reveal possible inconsistencies across the MB papers and hypotheses they express as they are all specified in the unifying OPM model.
Project and product are two complementary facets of the lifecycle of any complex man-made system. The project focuses on the early phases of the system to be delivered, deployed, and supported, while the product focuses on the system itself – its function, structure, and behavior. Conceptual modelling and design is a major area common to both the project and the product, since evidently, the product is the deliverable of the project. Traditionally, however, the project and the product entities have been addressed as separate domains, each with its dedicated approaches, methods, and tools. This separation has hindered the integration of the project with the product it delivers, missing potential tangible benefits for all the stakeholders involved. Systems Engineering Management (SEM) is an emerging practice that is being developed hand in hand with the maturation of systems engineering. Standards for SEM account for the intimate relationships between SEM and Project Management (PM) and highlight the criticality of these relationships in improving systems project management. While PM methods have traditionally focused on scheduling, budgeting, and scope management, SEM emphasizes the management of the project-product ensemble and issues related to the technologies of the system under development. The actual practice of systems engineering management involves continuous iterative zigzagging between the two domains – the systems engineering domain and the project management domain. This zigzagging is a cognitive process of understanding the intricate relationships between the product domain and the project domain, and planning the SEM efforts accordingly. What the product-project ensemble has been lacking is a common underlying ontology, a conceptual model, and a supporting software environment. Attaining these missing elements enables the simultaneous expression of the function, structure and behavior of the project and the product. This thesis presents a model-based approach to managing the lifecycle of the product to be developed hand-in-hand with the lifecycle of the project, within the scope of which the product is developed. The cornerstone of this Project-Product Lifecycle Management (PPLM) approach is an underlying holistic conceptual model, supported by software capabilities for an integrated project and product lifecycle environment. The concurrent project-product model, built on common ontological foundations, enables better management, making it possible to directly link entities in one subsystem to those in the other. The expected value of the holistic, integrated conceptual model is the provision of both superior product lifecycle engineering and project management capabilities, yielding significant cut in time to market, reduced risk, and higher product quality.