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A digital service ecosystem enables value creation and the co-development of services in a value network under a common ecosystem regulation. The ecosystem members are able to focus on their core competences and can strengthen their forces by co-operating; yet remaining able to act independently. However, due to regulated environment, the ecosystem elements-i.e. ecosystem members, capabilities, infrastructure and the existing ecosystem assets-have an influence on digital service engineering, especially in the service requirements engineering phase. The main contribution of this paper is to describe how to specify the requirements of digital services in a digital service ecosystem. To this aim, this paper introduces the basic definitions and elements of the digital service ecosystem, and a scenario-based service requirement engineering (RE) method for the digital service ecosystem. A practical example is given to illustrate the use of the RE method. The collected feedback from the RE method users highlights the user experiences on the advantages and limitations of the proposed method.

IntroductionMany big data systems have been developed and realised to provide end user services (Netflix, Facebook, Twitter, LinkedIn etc.). Also, underlying architectures and technologies of the enabling systems have been published [1-3], and RAs have been designed and proposed [4][5][6]. Edge/5G computing is an emerging technological field [7], and the first products are being shipped to the markets. However, the utilisation of machine learning (ML) as part of the edge computing infrastructure is still an area for further research [8]. Particularly, it should be understood, how data is collected, and how models are Abstract Background: Augmented reality, computer vision and other (e.g. network functions, Internet-of-Things (IoT)) use cases can be realised in edge computing environments with machine learning (ML) techniques. For realisation of the use cases, it has to be understood how data is collected, stored, processed, analysed, and visualised in big data systems. In order to provide services with low latency for end users, often utilisation of ML techniques has to be optimized. Also, software/service developers have to understand, how to develop and deploy ML models in edge computing environments. Therefore, architecture design of big data systems to edge computing environments may be challenging. Findings:The contribution of this paper is reference architecture (RA) design of a big data system utilising ML techniques in edge computing environments. An earlier version of the RA has been extended based on 16 realised implementation architectures, which have been developed to edge/distributed computing environments. Also, deployment of architectural elements in different environments is described. Finally, a system view is provided of the software engineering aspects of ML model development and deployment. Conclusions:The presented RA may facilitate concrete architecture design of use cases in edge computing environments. The value of RAs is reduction of development and maintenance costs of systems, reduction of risks, and facilitation of communication between different stakeholders.

A novel distributed middleware service platform, called MidGate platform, is presented in this paper. The central contribution is description of the developed MidGate platform and its architecture focusing especially on the adaptation, context‐awareness, and personalization of mobile and pervasive services. The research problem addressed is how to facilitate the development of interoperable applications and services into heterogeneous and distributed service gateway based environments. A requirement analysis of future mobile and pervasive services and key technologies has been carried out to establish a solid base and requirements for the development of the MidGate platform. The key mechanisms supporting adaptation, context‐awareness, and personalization of applications and services are presented. The novel middleware architecture solution of the MidGate platform utilizing these key mechanisms is also described. The MidGate architecture utilizes the emerging Generic Service Elements (GSE) approach, where generic and collectively utilizable services are provided to applications as middleware services that are part of a service platform. The main contribution of this research is the definition of a set of GSEs, the related MidGate platform architecture and its evaluation. The evaluation of the MidGate platform has been carried out in series of laboratory prototypes. The evaluation indicates that the MidGate platform solution is well applicable in various service gateway‐based distributed systems and extends well into resource‐constrained mobile environments.

This paper defines digital service in the context of technologically enhanced value co-creation between service system entities. Progress in digitalization and Artificial Intelligence (AI) is increasing the relative share of technologically enhanced value co-creation between service system entities (e.g., people, companies, nations). Highly automated technical systems increasingly act as autonomous agents, on behalf of service providers, in value co-creation interactions with the system users. Sufficient conceptualization, abstractions and modeling paradigms for research and development of this type of value co-creation are absent from the literature and introduced in this paper. The main contribution of the paper is introduction and definition of digital service and digital service membrane as fundamental concepts in service science and service systems, with directions for future research on the topic.

The contribution of this paper is a Generic Communication Middleware (GCM) concept and architecture definition for application messaging in heterogeneous distributed computing environments. The GCM is targeted to facilitate the development of distributed applications into heterogeneous computing environments, with special attention given to applicability for both wireless and wired communication and variable capability devices. The requirements are gathered from the literature and initial prototype implementations. The GCM concept and architecture are presented in the paper. The novelty of the GCM middleware is that it provides both application and transport independent messaging system architecture that can be widely applied in different applications and services as middleware. Functional validation of the GCM concept and architecture is provided via prototypes overviewed and empirically analyzed in the paper.

An increasing amount of today's software systems is developed by dynamically composing available atomic services to form a single service that responds to consumers' demand. These composite services are distributed across the network, adapted dynamically during runtime, and still required to work correctly and be available on demand. The development of these kinds of modern services requires new modeling and analysis methods and techniques to enable service reliability during run-time. In this paper, we define the required phases of the composite service design and execution to achieve reliable composite service. These phases are described in the form of a framework. We perform a literature survey of existing methods and approaches for reliable composite services to find out how they match with the criteria of our framework. The contribution of the work is to reveal the current status in the research field of reliable composite service engineering.

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