Decision-Making Support for Data Integration in Cyber-Physical-System Architectures

Cyber-Physical Systems (CPS) design is a complex challenge involving physical and digital components working together to accomplish a specific goal. Integrating such systems involves combining data from various distributed Internet of Things (IoT) devices and cloud services to create meaningful insights and actions. Service-based IoT data integration involves several steps: collection, processing, analysis, and visualization. Adopting a holistic approach that considers physical and digital aspects is crucial when designing data integration in distributed CPS. Architectural design decisions are vital in shaping a CPS' functionality and system qualities, such as performance, security, and reliability. Although several patterns and practices for CPS architecture have been proposed, much of the knowledge in this area is informally discussed in the grey literature, e.g., in practitioner blogs and system documentation. As a result, this architectural knowledge is dispersed across many sources that are often inconsistent and based on personal experience. In this study, we present the results of a qualitative, in-depth study of the best practices and patterns of distributed CPS architecture as described by practitioners. We have developed a formal architecture decision model using a model-based qualitative research method. We aim to bridge the science-practice gap, enhance comprehension of practitioners' CPS approaches, and provide decision-making support

Decision-Making Support for Data Integration in Cyber-Physical-System Architectures

Cyber-Physical Systems (CPS) design is a complex challenge involving physical and digital components working together to accomplish a specific goal. Integrating such systems involves combining data from various distributed Internet of Things (IoT) devices and cloud services to create meaningful insights and actions. Service-based IoT data integration involves several steps: collection, processing, analysis, and visualization. Adopting a holistic approach that considers physical and digital aspects is crucial when designing data integration in distributed CPS. Architectural design decisions are vital in shaping a CPS' functionality and system qualities, such as performance, security, and reliability. Although several patterns and practices for CPS architecture have been proposed, much of the knowledge in this area is informally discussed in the grey literature, e.g., in practitioner blogs and system documentation. As a result, this architectural knowledge is dispersed across many sources that are often inconsistent and based on personal experience. In this study, we present the results of a qualitative, in-depth study of the best practices and patterns of distributed CPS architecture as described by practitioners. We have developed a formal architecture decision model using a model-based qualitative research method. We aim to bridge the science-practice gap, enhance comprehension of practitioners' CPS approaches, and provide decision-making support

Quality-aware model-driven service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services

Ontology-based patterns for the integration of business processes and enterprise application architectures

Increasingly, enterprises are using Service-Oriented Architecture (SOA) as an approach to Enterprise Application Integration (EAI). SOA has the potential to bridge the gap between business and technology and to improve the reuse of existing applications and the interoperability with new ones. In addition to service architecture descriptions, architecture abstractions like patterns and styles capture design knowledge and allow the reuse of successfully applied designs, thus improving the quality of software. Knowledge gained from integration projects can be captured to build a repository of semantically enriched, experience-based solutions. Business patterns identify the interaction and structure between users, business processes, and data. Specific integration and composition patterns at a more technical level address enterprise application integration and capture reliable architecture solutions. We use an ontology-based approach to capture architecture and process patterns. Ontology techniques for pattern definition, extension and composition are developed and their applicability in business process-driven application integration is demonstrated

Pattern-based software architecture for service-oriented software systems

Service-oriented architecture is a recent conceptual framework for service-oriented software platforms. Architectures are of great importance for the evolution of software systems. We present a modelling and transformation technique for service-centric distributed software systems. Architectural configurations, expressed through hierarchical architectural patterns, form the core of a specification and transformation technique. Patterns on different levels of abstraction form transformation invariants that structure and constrain the transformation process. We explore the role that patterns can play in architecture transformations in terms of functional properties, but also non-functional quality aspects