An interactive metaheuristic search framework for software serviceidentification from business process models

In recent years, the Service-Oriented Architecture (SOA) model of computing has become widely used and has provided efficient and agile business solutions in response to inevitable and rapid changes in business requirements. Software service identification is a crucial component in the production of a service-oriented architecture and subsequent successful software development, yet current service identification methods have limitations. For example, service identification methods are either not sufficiently comprehensive to handle the totality of service identification activities, or they lack computational support, or they pay insufficient attention to quality checks of resulting services. To address these limitations, comprehensive computationally intelligent support for software engineers when deriving software services from an organisation’s business process models shows great potential, especially when the impact of human preference on the quality of the resulting solutions can be incorporated. Accordingly, this research attempts to apply interactive metaheuristic search to effectively bridge the gap between business and SOA technology and so increase business agility.A novel, comprehensive framework is introduced that is driven by domain independent role-based business process models, and uses an interactive metaheuristic search-based service identification approach based on a genetic algorithm, while adhering to SOA principles. Termed BPMiSearch, the framework is composed of three main layers. The first layer is concerned with processing inputs from business process models into search space elements by modelling input data and presenting them at an appropriate level of granularity. The second layer focuses on identifying software services from the specified search space. The third layer refines the resulting services to map the business elements in the resulting candidate services to the corresponding service components. The proposed BPMiSearch framework has been evaluated by applying it to a healthcare domain case study, specifically, Cancer Care and Registration (CCR) business processes at the King Hussein Cancer Centre, Amman, Jordan.Experiments show that the impact of software engineer interaction on the quality of the outcomes in terms of search effectiveness, efficiency, and level of user satisfaction, is assessed. Results show that BPMiSearch has rapid search performance to positively support software engineers in the identification of services from role-based business process models while adhering to SOA principles. High-quality services are identified that might not have been arrived at manually by software engineers. Furthermore, it is found that BPMiSearch is sensitive and responsive to software engineer interaction resulting in a positive level of user trust, acceptance, and satisfaction with the candidate services

Business process trends

Business process and business process management (BPM) concepts have matured over the years and new technology, concepts, standards and solutions appear. In this chapter we will therefore focus on the current and future process trends. We will elaborate on the importance of trends, the maturity of the subject, giving a perspective on what emerging trends, industry trends, mega trends are, what is hyped at the moment, and what has reached a market adoption where it has started to become the de facto standard in terms of mega trends that has achieved a dominant position by public acceptance

Smart Sustainable Manufacturing Systems

With the advent of disruptive digital technologies, companies are facing unprecedented challenges and opportunities. Advanced manufacturing systems are of paramount importance in making key enabling technologies and new products more competitive, affordable, and accessible, as well as for fostering their economic and social impact. The manufacturing industry also serves as an innovator for sustainability since automation coupled with advanced manufacturing technologies have helped manufacturing practices transition into the circular economy. To that end, this Special Issue of the journal Applied Sciences, devoted to the broad field of Smart Sustainable Manufacturing Systems, explores recent research into the concepts, methods, tools, and applications for smart sustainable manufacturing, in order to advance and promote the development of modern and intelligent manufacturing systems. In light of the above, this Special Issue is a collection of the latest research on relevant topics and addresses the current challenging issues associated with the introduction of smart sustainable manufacturing systems. Various topics have been addressed in this Special Issue, which focuses on the design of sustainable production systems and factories; industrial big data analytics and cyberphysical systems; intelligent maintenance approaches and technologies for increased operating life of production systems; zero-defect manufacturing strategies, tools and methods towards online production management; and connected smart factories

Towards Interoperable Research Infrastructures for Environmental and Earth Sciences

This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions