Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system

Cognitive Business Process Management for Adaptive Cyber-Physical Processes

In the era of Big Data and Internet-of-Things (IoT), all real-world environments are gradually becoming cyber-physical (e.g., emergency management, healthcare, smart manufacturing, etc.), with the presence of connected devices and embedded ICT systems (e.g., smartphones, sensors, actuators) producing huge amounts of data and events that influence the enactment of the Cyber Physical Processes (CPPs) enacted in such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention at run-time. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS that combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on well-established action-based formalisms in Artificial Intelligence, which allow to interpret the ever-changing knowledge of cyber-physical environments and to adapt CPPs by preserving their base structure.Comment: Preprint from Proceedings of 1st International Workshop on Cognitive Business Process Management (CBPM 2017

Augmented Business Process Management Systems: A Research Manifesto

Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems that draws upon trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.Comment: 19 pages, 1 figur

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics

Dynamic digital factories for agile supply chains: An architectural approach

Digital factories comprise a multi-layered integration of various activities along the factories and product lifecycles. A central aspect of a digital factory is that of enabling the product lifecycle stakeholders to collaborate through the use of software solutions. The digital factory thus expands outside the company boundaries and offers the opportunity to collaborate on business processes affecting the whole supply chain. This paper discusses an interoperability architecture for digital factories. To this end, it delves into the issue by analysing the key requirements for enabling a scalable factory architecture characterized by access to services, aggregation of data, and orchestration of production processes. Then, the paper revises the state-of-the-art w.r.t. these requirements and proposes an architectural framework conjugating features of both service-oriented and data-sharing architectures. The framework is exemplified through a case study

From distributed coordination to field calculus and aggregate computing

open6siThis work has been partially supported by: EU Horizon 2020 project HyVar (www.hyvar-project .eu), GA No. 644298; ICT COST Action IC1402 ARVI (www.cost -arvi .eu); Ateneo/CSP D16D15000360005 project RunVar (runvar-project.di.unito.it).Aggregate computing is an emerging approach to the engineering of complex coordination for distributed systems, based on viewing system interactions in terms of information propagating through collectives of devices, rather than in terms of individual devices and their interaction with their peers and environment. The foundation of this approach is the distillation of a number of prior approaches, both formal and pragmatic, proposed under the umbrella of field-based coordination, and culminating into the field calculus, a universal functional programming model for the specification and composition of collective behaviours with equivalent local and aggregate semantics. This foundation has been elaborated into a layered approach to engineering coordination of complex distributed systems, building up to pragmatic applications through intermediate layers encompassing reusable libraries of program components. Furthermore, some of these components are formally shown to satisfy formal properties like self-stabilisation, which transfer to whole application services by functional composition. In this survey, we trace the development and antecedents of field calculus, review the field calculus itself and the current state of aggregate computing theory and practice, and discuss a roadmap of current research directions with implications for the development of a broad range of distributed systems.embargoed_20210910Viroli, Mirko; Beal, Jacob; Damiani, Ferruccio; Audrito, Giorgio; Casadei, Roberto; Pianini, DaniloViroli, Mirko; Beal, Jacob; Damiani, Ferruccio; Audrito, Giorgio; Casadei, Roberto; Pianini, Danil

Self-managed Workflows for Cyber-physical Systems

Workflows are a well-established concept for describing business logics and processes in web-based applications and enterprise application integration scenarios on an abstract implementation-agnostic level. Applying Business Process Management (BPM) technologies to increase autonomy and automate sequences of activities in Cyber-physical Systems (CPS) promises various advantages including a higher flexibility and simplified programming, a more efficient resource usage, and an easier integration and orchestration of CPS devices. However, traditional BPM notations and engines have not been designed to be used in the context of CPS, which raises new research questions occurring with the close coupling of the virtual and physical worlds. Among these challenges are the interaction with complex compounds of heterogeneous sensors, actuators, things and humans; the detection and handling of errors in the physical world; and the synchronization of the cyber-physical process execution models. Novel factors related to the interaction with the physical world including real world obstacles, inconsistencies and inaccuracies may jeopardize the successful execution of workflows in CPS and may lead to unanticipated situations. This thesis investigates properties and requirements of CPS relevant for the introduction of BPM technologies into cyber-physical domains. We discuss existing BPM systems and related work regarding the integration of sensors and actuators into workflows, the development of a Workflow Management System (WfMS) for CPS, and the synchronization of the virtual and physical process execution as part of self-* capabilities for WfMSes. Based on the identified research gap, we present concepts and prototypes regarding the development of a CPS WFMS w.r.t. all phases of the BPM lifecycle. First, we introduce a CPS workflow notation that supports the modelling of the interaction of complex sensors, actuators, humans, dynamic services and WfMSes on the business process level. In addition, the effects of the workflow execution can be specified in the form of goals defining success and error criteria for the execution of individual process steps. Along with that, we introduce the notion of Cyber-physical Consistency. Following, we present a system architecture for a corresponding WfMS (PROtEUS) to execute the modelled processes-also in distributed execution settings and with a focus on interactive process management. Subsequently, the integration of a cyber-physical feedback loop to increase resilience of the process execution at runtime is discussed. Within this MAPE-K loop, sensor and context data are related to the effects of the process execution, deviations from expected behaviour are detected, and compensations are planned and executed. The execution of this feedback loop can be scaled depending on the required level of precision and consistency. Our implementation of the MAPE-K loop proves to be a general framework for adding self-* capabilities to WfMSes. The evaluation of our concepts within a smart home case study shows expected behaviour, reasonable execution times, reduced error rates and high coverage of the identified requirements, which makes our CPS~WfMS a suitable system for introducing workflows on top of systems, devices, things and applications of CPS.:1. Introduction 15 1.1. Motivation 15 1.2. Research Issues 17 1.3. Scope & Contributions 19 1.4. Structure of the Thesis 20 2. Workflows and Cyber-physical Systems 21 2.1. Introduction 21 2.2. Two Motivating Examples 21 2.3. Business Process Management and Workflow Technologies 23 2.4. Cyber-physical Systems 31 2.5. Workflows in CPS 38 2.6. Requirements 42 3. Related Work 45 3.1. Introduction 45 3.2. Existing BPM Systems in Industry and Academia 45 3.3. Modelling of CPS Workflows 49 3.4. CPS Workflow Systems 53 3.5. Cyber-physical Synchronization 58 3.6. Self-* for BPM Systems 63 3.7. Retrofitting Frameworks for WfMSes 69 3.8. Conclusion & Deficits 71 4. Modelling of Cyber-physical Workflows with Consistency Style Sheets 75 4.1. Introduction 75 4.2. Workflow Metamodel 76 4.3. Knowledge Base 87 4.4. Dynamic Services 92 4.5. CPS-related Workflow Effects 94 4.6. Cyber-physical Consistency 100 4.7. Consistency Style Sheets 105 4.8. Tools for Modelling of CPS Workflows 106 4.9. Compatibility with Existing Business Process Notations 111 5. Architecture of a WfMS for Distributed CPS Workflows 115 5.1. Introduction 115 5.2. PROtEUS Process Execution System 116 5.3. Internet of Things Middleware 124 5.4. Dynamic Service Selection via Semantic Access Layer 125 5.5. Process Distribution 126 5.6. Ubiquitous Human Interaction 130 5.7. Towards a CPS WfMS Reference Architecture for Other Domains 137 6. Scalable Execution of Self-managed CPS Workflows 141 6.1. Introduction 141 6.2. MAPE-K Control Loops for Autonomous Workflows 141 6.3. Feedback Loop for Cyber-physical Consistency 148 6.4. Feedback Loop for Distributed Workflows 152 6.5. Consistency Levels, Scalability and Scalable Consistency 157 6.6. Self-managed Workflows 158 6.7. Adaptations and Meta-adaptations 159 6.8. Multiple Feedback Loops and Process Instances 160 6.9. Transactions and ACID for CPS Workflows 161 6.10. Runtime View on Cyber-physical Synchronization for Workflows 162 6.11. Applicability of Workflow Feedback Loops to other CPS Domains 164 6.12. A Retrofitting Framework for Self-managed CPS WfMSes 165 7. Evaluation 171 7.1. Introduction 171 7.2. Hardware and Software 171 7.3. PROtEUS Base System 174 7.4. PROtEUS with Feedback Service 182 7.5. Feedback Service with Legacy WfMSes 213 7.6. Qualitative Discussion of Requirements and Additional CPS Aspects 217 7.7. Comparison with Related Work 232 7.8. Conclusion 234 8. Summary and Future Work 237 8.1. Summary and Conclusion 237 8.2. Advances of this Thesis 240 8.3. Contributions to the Research Area 242 8.4. Relevance 243 8.5. Open Questions 245 8.6. Future Work 247 Bibliography 249 Acronyms 277 List of Figures 281 List of Tables 285 List of Listings 287 Appendices 28

Model-Based Engineering of Collaborative Embedded Systems

This Open Access book presents the results of the "Collaborative Embedded Systems" (CrESt) project, aimed at adapting and complementing the methodology underlying modeling techniques developed to cope with the challenges of the dynamic structures of collaborative embedded systems (CESs) based on the SPES development methodology. In order to manage the high complexity of the individual systems and the dynamically formed interaction structures at runtime, advanced and powerful development methods are required that extend the current state of the art in the development of embedded systems and cyber-physical systems. The methodological contributions of the project support the effective and efficient development of CESs in dynamic and uncertain contexts, with special emphasis on the reliability and variability of individual systems and the creation of networks of such systems at runtime. The project was funded by the German Federal Ministry of Education and Research (BMBF), and the case studies are therefore selected from areas that are highly relevant for Germany’s economy (automotive, industrial production, power generation, and robotics). It also supports the digitalization of complex and transformable industrial plants in the context of the German government's "Industry 4.0" initiative, and the project results provide a solid foundation for implementing the German government's high-tech strategy "Innovations for Germany" in the coming years