Model Transformation Development Using Automated Requirements Analysis, Metamodel Matching, and Transformation by Example

In this article, we address how the production of model transformations (MT) can be accelerated by automation of transformation synthesis from requirements, examples, and metamodels. We introduce a synthesis process based on metamodel matching, correspondence patterns between metamodels, and completeness and consistency analysis of matches. We describe how the limitations of metamodel matching can be addressed by combining matching with automated requirements analysis and model transformation by example (MTBE) techniques. We show that in practical examples a large percentage of required transformation functionality can usually be constructed automatically, thus potentially reducing development effort. We also evaluate the efficiency of synthesised transformations. Our novel contributions are: The concept of correspondence patterns between metamodels of a transformation. Requirements analysis of transformations using natural language processing (NLP) and machine learning (ML). Symbolic MTBE using “predictive specification” to infer transformations from examples. Transformation generation in multiple MT languages and in Java, from an abstract intermediate language.

Generic Model for Adaptive Systems Realization

The permanent acquisition of the technical environment state and the ability to react to changes in this environment as well as to adapt to it are nowadays crucial for any information system. In this article, the authors present a well-defined model to guarantee in a simple way the design and the realization of adaptive information systems. This model is based on the Unified Modeling Language (UML) which is a widely known modeling standard. Its coverage is limited to bringing out the graded parties in the design of adaptive information systems. A future definition of a metamodel less related to UML language is therefore possible. The authors also present a code generator based on a model transformation technique. This generator allows you to partially produce domain-specific code as needed. A more complete code generator will come to ensure automatic generation of the code.

Generic Model for Adaptive Systems Realization

The permanent acquisition of the technical environment state and the ability to react to changes in this environment as well as to adapt to it are nowadays crucial for any information system. In this article, the authors present a well-defined model to guarantee in a simple way the design and the realization of adaptive information systems. This model is based on the Unified Modeling Language (UML) which is a widely known modeling standard. Its coverage is limited to bringing out the graded parties in the design of adaptive information systems. A future definition of a metamodel less related to UML language is therefore possible. The authors also present a code generator based on a model transformation technique. This generator allows you to partially produce domain-specific code as needed. A more complete code generator will come to ensure automatic generation of the code.

Strategic Growth of Everything-as-a-Service (XaaS) Business Model Transformation

The study presents the XaaS architecture and explores how this business model has strategically developed and how it has revolutionized the whole business industry. The study indicates how this particular business model allows companies to enhance the range of advantages of Internet Cloud model applications, particularly when it deals with process of media processing and customization of users. Its huge benefits with negligible disadvantages are mentioned as part of the presented research. This study will help to understand the services of XaaS model, challenges experiencing by it and its future opportunities. This will also assists new scholars of field to build a better understanding and better prospects regarding XaaS in future.

Optimal Placement of PMU for Fast Robust Power System Dynamic State Estimation using UKF–GBDT Technique

This paper proposes a fast and robust dynamic state estimation technique based on model transformation method using the proposed hybrid technique. The proposed hybrid method is the combination of Unscented Kalman Filter (UKF) and Gradient Boosting Decision Tree (GBDT), hence commonly referred to as the UKF–GBDT technique. The proposed model transformation approach is accomplished by taking the active power generator measured as input variable and derived frequency as rate of change of frequency measurements of phasor measurement units (PMU) as dynamic generator output variable model. The proposed hybrid technique is also formulated to deal with data quality issues, and the rate of change of frequency and frequency measurements may be skewed in the presence of rigorous disruption or communication problems. This permits to obtain discrete-time linear dynamic equations in state space based on the linear Kalman filter (LKF). With this proper control, this model alleviates filter divergence problems, which can be a severe issue if the nonlinear model is utilized in greatly strained operating system conditions, and gives quick estimate of rotor speeds together with angles through transient modes if only the transient stability with control is concerned. In the case of long-term dynamics, the outcome of governor’s response in long-term system dynamics is offset together with mechanical power at rotor speed and the state vector angles for joint evaluation. At last, the performance of the proposed method is simulated in MATLAB/Simulink and the performance is compared to the existing methods like UKF, GBDT and ANN. The proposed technique is simulated under three case studies like IEEE 14-, 30- and 118-bus systems.

Business Model Transformation and Business Process Reengineering in Information Technology Companies in Nigeria

Purpose: This study examined the relationship between business model transformation and business process reengineering of information technology companies in Nigeria. Methodology: The study adopted a cross-sectional research survey. The population of the study was the 4 major information technology companies that met the capitalization base condition as listed in Nigeria Stock Exchange. Questionnaire was the major instrument for data collection and the pilot survey was distributed to 50 respondents to help ascertain the possible response outcome on the long run if the entire respondents are examined. A Cronbach alpha of 0.7 was used to determine the level of reliability of the research instrument. The hypotheses were tested using the Spearman Rank Order Correlation Coefficient with the aid of Statistical Package for Social Sciences version 23.0. Results The findings revealed that there is a significant relationship between business model transformation and business process reengineering of information technology companies in Nigeria. The study specifically found that there is a significant relationship between business model transformation and process formation in information technology companies in Nigeria. Also, the study found that there is a significant relationship between business model transformation and enhanced capabilities in information technology companies in Nigeria. Finally, the study found that that there is a significant relationship between business model transformation and efficiency improvements in information technology companies in Nigeria. Based on the study findings, the researchers conclude that domain transformation significantly relate with business process reengineering in information technology companies in Nigeria. Unique contribution: The study recommends that preference to expertise can be emphasized through the acknowledgement and appreciation of skill and creativity within the workplace in a manner that recognizes and encourages knowledge development and skill upgrades within the organization, thereby driving competence in the workforce of the organization.

Governance Drivers and Barriers for Business Model Transformation in the Energy Sector

Smart second-generation policies for energy transition governance have been less studied and reviewed in the literature. They are also difficult to compare or measure in terms of their effectiveness with regard to the energy transition, not only because each country’s objectives and underlying drivers for an energy transition are different. Technological innovation and new technology deployment are only the tip of the iceberg. Understanding how to redesign energy governance to allow for business model reconfiguration among incumbents and how to stimulate business model innovation by start-ups and new entrants is key for an effective and sustainable energy transition in the long term. However, beyond this, countries must address the underlying driving forces such as consumption patterns and the financial system. Therefore, business model transformation is not the only solution, but it is an important one and it requires well-designed policies. It also requires the involvement of all stakeholders at all levels of the economic fabric of each region and country. At the same time, we continue to measure progress on energy transitions in a superficial and extremely limited way. Policies must now be smarter, not just more ambitious in terms of appearances, and the measurement of energy transition progress must evolve as well. We discuss the full story of an energy transition to the extent possible in a single chapter. For example, we will review business models in different sub-sectors, policies that either block or promote such changes in each sub-sector chosen, and the elements that are necessary for energy transitions to become successful and sustainable without long-term government intervention and financial support. Finally, we also provide insights from an expert workshop held in 2019 and we outline our upcoming work on an Energy Transition Preparedness Index.

Contrasting dedicated model transformation languages versus general purpose languages: a historical perspective on ATL versus Java based on complexity and size

Model transformations are among the key concepts of model-driven engineering (MDE), and dedicated model transformation languages (MTLs) emerged with the popularity of the MDE pssaradigm about 15 to 20 years ago. MTLs claim to increase the ease of development of model transformations by abstracting from recurring transformation aspects and hiding complex semantics behind a simple and intuitive syntax. Nonetheless, MTLs are rarely adopted in practice, there is still no empirical evidence for the claim of easier development, and the argument of abstraction deserves a fresh look in the light of modern general purpose languages (GPLs) which have undergone a significant evolution in the last two decades. In this paper, we report about a study in which we compare the complexity and size of model transformations written in three different languages, namely (i) the Atlas Transformation Language (ATL), (ii) Java SE5 (2004–2009), and (iii) Java SE14 (2020); the Java transformations are derived from an ATL specification using a translation schema we developed for our study. In a nutshell, we found that some of the new features in Java SE14 compared to Java SE5 help to significantly reduce the complexity of transformations written in Java by as much as 45%. At the same time, however, the relative amount of complexity that stems from aspects that ATL can hide from the developer, which is about 40% of the total complexity, stays about the same. Furthermore we discovered that while transformation code in Java SE14 requires up to 25% less lines of code, the number of words written in both versions stays about the same. And while the written number of words stays about the same their distribution throughout the code changes significantly. Based on these results, we discuss the concrete advancements in newer Java versions. We also discuss to which extent new language advancements justify writing transformations in a general purpose language rather than a dedicated transformation language. We further indicate potential avenues for future research on the comparison of MTLs and GPLs in a model transformation context.