The Service-Oriented Architecture has rapidly become the de facto standard for modern information systems. Although recently considerable research attention has been paid to the management of services, several gaps can still be observed. Service management as far as it is automated is either mixed up with the operational service logic itself, or handled in a separate not serviceoriented system, such as a BAM platform. In addition, there is a growing business demand for value-driven service management. In this paper, a general framework for management service design is presented that covers both business services and software services and is rooted in the business ontology REA, extended with a REA management ontology. The framework is applied to two different case studies, one in the Italian wine industry and one related to a robot cleaner.

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Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy.

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User involvement in the process of discovering and shaping the product is the base of software systems. In recent years, however, a shift in the user feedback has been observed: repositories of user data have become increasingly more subjected to analysis for improvement purposes. Significant surge has been seen in feedback collected from users in the form of reviews and ratings along with app usage statistics. This led software engineering researchers to deploy big data analytics techniques in order to figure out the requirements that should be met in the future software system releases. While a variety of big data analytics methods exist, it is not clear which ones have been used and what are the benefits and disadvantages of these proposals. In this paper, we have aimed to outline the recently published proposals for big data analytics techniques for user feedback analysis. We found that the majority of the techniques rest on natural language processing concepts and visualization. Our findings also indicate that the majority of the proposals come from the United States, Germany and the United Kingdom. Moreover, we also found the proposed techniques perform well with the chosen datasets however the generalizability and scalability of these method raised concerns as these methods are not evaluated based on real-world cases.