Digital Twin of the Organization (DTO) is a relatively new concept that emerged to help managers have a full understanding of their organization and realize their objectives. Indeed, DTO enables connecting all the elements of an organization virtually by providing monitoring, optimization, prediction, and other capabilities through continuous simulations. Creating a flexible and evolvable DTO that covers and supports the organization's business strategies is a complex and time-consuming task that requires engineering best practices. In this context, this paper presents and evaluates the EA Blueprint Pattern, which serves as an architectural reference for the development of a DTO by allowing for mapping well-known Enterprise Architecture concepts into software components defining the DTO software architecture. The evaluation is carried on by showing how to use the pattern for creating the DTO for a given organization. Then, a thorough discussion is conducted to analyze how the developed DTO should evolve to deal with vertical and horizontal integration. The lessons learned highlight the strengths and weaknesses along with practical implications for organizations that are eager to develop their DTO according to the EA Blueprint Pattern.

The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.

Robots are increasingly adopted in a wide range of unstructured and uncertain environments, where they are expected to keep quality properties such as efficiency, accuracy, and safety. To this end, robots need to be smart and continuously update their situation awareness. Self-adaptive systems pave the way for accomplishing this aim by enabling a robot to understand its surroundings and adapt to various scenarios in a systematic manner. However, some situations, e.g., adjusting adaptation rules, refining run-time models, narrowing a vast adaptation domain, and taking future scenarios into consideration, etc. may require the self-adaptive system to include additional specialized components. In this regard, this work proposes a novel approach combining the MAPE-K, adaptive controllers, and a Digital Twin of the robot to enable the managing system to be aware of new scenarios appearing at run-time and operate safely, accurately, and efficiently. A state-of-the-art robot model is employed to evaluate the suitability of the approach.

Architecting a self-adaptive system with decentralized control is challenging. Indeed, architects shall consider several different and interdependent design dimensions and devise multiple control loops to coordinate and timely perform the correct adaptations. To support this task, we propose Decor, a reasoning framework for architecting and evaluating decentralized control. Decor provides (i) multi-paradigm modeling support, (ii) a modeling environment for MAPE-K style decentralized control, and (iii) a co-simulation environment for simulating the decentralized control together with the managed system and estimating the quality attributes of interest. We apply the Decor in three case studies: an intelligent transportation system, a smart power grid, and a cloud computing application. The studies demonstrate the framework’s capabilities to support informed architectural decisions on decentralized control and adaptation strategies.

The Internet of Things (IoT) is rapidly developing in diverse and critical applications such as environmental sensing andindustrial control systems. IoT devices can be very heterogeneous in terms of hardware and software architectures, communication protocols, and/or manufacturers. Therefore, when those devices are connected together to build a complex system,detecting and fxing any anomalies can be very challenging. In this paper, we explore a relatively novel technique known asProcess Mining, which—in combination with log-fle analytics and machine learning—can support early diagnosis, prognosis, and subsequent automated repair to improve the resilience of IoT devices within possibly complex cyber-physicalsystems. Issues addressed in this paper include generation of consistent Event Logs and defnition of a roadmap towardefective Process Discovery and Conformance Checking to support Self-Healing in IoT.

Service discovery mechanisms have continuously evolved during the last years to support the effective and efficient service composition in large-scale microservice applications. Still, the dynamic nature of services (and of their contexts) are being rarely taken into account for maximizing the desired quality of service. This paper proposes using machine learning techniques, as part of the service discovery process, to select microservice instances in a given context, maximize QoS, and take into account the continuous changes in the execution environment. Both deep neural networks and reinforcement learning techniques are used. Experimental results show how the proposed approach outperforms traditional service discovery mechanisms.

With the advancing digitisation of society and industry we observe a progressing blending of computational, physical, and social processes. The trustworthiness and sustainability of these systems will be vital for our society. However, engineering modern computing systems is complex as they have to: i) operate in uncertain and continuously changing environments, ii) deal with huge amounts of data, and iii) require seamless interaction with human operators. To that end, we argue that both systems and the way we engineer them must become smarter. With smarter we mean that systems and engineering processes adapt and evolve themselves through a perpetual process that continuously improves their capabilities and utility to deal with the uncertainties and amounts of data they face. We highlight key engineering areas: cyber-physical systems, self-adaptation, data-driven technologies, and visual analytics, and outline key challenges in each of them. From this, we propose a research agenda for the years to come.

Advancements in Cyber-Physical Systems, IoT, Data-driven methods, and networking prepare the adequate infrastructure for constructing new organizations, where everything is connected and interact with each other. A Digital Twin of the Organization (DTO) exploits these infrastructures to provide an accurate digital representation of the organization. Beyond the usual representation of devices, machines and physical assets supplied by Digital Twins, a DTO also include processes, services, people, roles, and all other relevant elements for the operation of organizations. Therefore, DTO can play a key role in realizing and analyzing aspects of organizations, assisting managers on the knowledge of the organization status, and foreseeing possible effects of potential changes in the organization. However, due to the dynamic, open, and ever-changing environment of organizations, an established DTO will soon degrade or even lose all its utility. Therefore, a DTO needs to evolve to recover its utility when the organization changes. The development of flexible, resilient, and easy to evolve DTO has not been well-addressed yet. Most of the existing proposals are context-dependent, system-specific, or focus on providing solutions in high-level abstraction. This work leverages Enterprise Architecture to propose an architectural pattern to serve as a blueprint toward the development of resilient DTO.

Modern information systems connecting software, physical systems and people, are usually characterized by high dynamism. These dynamics introduce uncertainties, which in turn may harm the quality of systems and lead to incomplete, inaccurate, and unreliable results. To deal with this issue, in this paper we report our incremental experience on the usage of different performance modelling notations while analyzing Intelligent Transportation Systems. More specifically, Queueing Networks and Petri Nets have been adopted and interesting insights are derived.

Human-Cyber-Physical-Systems (HPCS), such as critical infrastructures in modern society, are subject to several systemic threats due to their complex interconnections and interdependencies. Management of systemic threats requires a paradigm shift from static risk assessment to holistic resilience modeling and evaluation using intelligent, data-driven and run-time approaches. In fact, the complexity and criticality of HCPS requires timely decisions considering many parameters and implications, which in turn require the adoption of advanced monitoring frameworks and evaluation tools. In order to tackle such challenge, we introduce those new paradigms in a framework named RESILTRON, envisioning Digital Twins (DT) to support decision making and improve resilience in HCPS under systemic stress. In order to represent possibly complex and heterogeneous HCPS, together with their environment and stressors, we leverage on multi-simulation approaches, combining multiple formalisms, data-driven approaches and Artificial Intelligence (AI) modelling paradigms, through a structured, modular and compositional framework. DT are used to provide an adaptive abstract representation of the system in terms of multi-layered spatially-embedded dynamic networks, and to apply self-adaptation to time-warped What-If analyses, in order to find the best sequence of decisions to ensure resilience under uncertainty and continuous HPCS evolution.