Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

Is this Digital Resilience? Insights from Adaptation and Exaptation of a Cyber-Physical-Social System

This paper is based on a qualitative case study that explores the adaptation and customisation of a Cyber Physical Social System (CPSS)-based patient monitoring solution for use during Covid19 in the Norwegian health sector. The study seeks to answer the following research questions: 1) what are the preconditions that enable the adaptive use of a CPSS in crisis response efforts? 2) what are the contributions of the adaptive use of technology in the building of digital resilience in a health organisation? The study identifies five main themes emerge as enabling factors forming a basis for the preconditions to adaptive use of the CPSS. We conclude with a discussion on the practical and theoretical implications of this research and how it contributes to crisis management and digital resilience theory

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

The CitySPIN Platform: A CPSS Environment for City-Wide Infrastructures

Cyber-physical Social System (CPSS) are complex systems that span the boundaries of the cyber, physical and social spheres. They play an important role in a variety of domains ranging from industry to smart city applications. As such, these systems necessarily need to take into account, combine and make sense of heterogeneous data sources from legacy systems, from the physical layer and also the social groups that are part of/use the system. The collection, cleansing and integration of these data sources represents a major effort not only during the operation of the system, but also during its engineering and design. Indeed, while ongoing efforts are concerned primarily with the operation of such systems, limited focus has been put on supporting the engineering phase of CPSS. To address this shortcoming, within the CitySPIN project we aim to create a platform that supports stakeholders involved in the design of these systems especially in terms of support for data management. To that end, we develop methods and techniques based on Semantic Web and Linked Data technologies for the acquisition and integration of heterogeneous data from disparate structured, semi-structured and unstructured sources, including open data and social data. In this paper we present the overall system architecturewith a core focus on data acquisition and integration.We demon-strate our approach through a prototypical implementation of an adaptive planning use case for public transportation scheduling

Falsification of Cyber-Physical Systems with Robustness-Guided Black-Box Checking

For exhaustive formal verification, industrial-scale cyber-physical systems (CPSs) are often too large and complex, and lightweight alternatives (e.g., monitoring and testing) have attracted the attention of both industrial practitioners and academic researchers. Falsification is one popular testing method of CPSs utilizing stochastic optimization. In state-of-the-art falsification methods, the result of the previous falsification trials is discarded, and we always try to falsify without any prior knowledge. To concisely memorize such prior information on the CPS model and exploit it, we employ Black-box checking (BBC), which is a combination of automata learning and model checking. Moreover, we enhance BBC using the robust semantics of STL formulas, which is the essential gadget in falsification. Our experiment results suggest that our robustness-guided BBC outperforms a state-of-the-art falsification tool.Comment: Accepted to HSCC 202