The business processes of organizations may deviate from normal control flow due to disruptive anomalies, including unknown, skipped, and wrongly-ordered activities. To identify these control-flow anomalies, process mining can check control-flow correctness against a reference process model through conformance checking, an explainable set of algorithms that allows linking any deviations with model elements. However, the effectiveness of conformance checking-based techniques is negatively affected by noisy event data and low-quality process models. To address these shortcomings and support the development of competitive and explainable conformance checking-based techniques for control-flow anomaly detection , we propose a novel process mining-based feature extraction approach with alignment-based conformance checking. This variant aligns the deviating control flow with a reference process model; the resulting alignment can be inspected to extract additional statistics such as the number of times a given activity caused mismatches. We integrate this approach into a flexible and explainable framework for developing techniques for control-flow anomaly detection . The framework combines process mining-based feature extraction and dimensionality reduction to handle high-dimensional feature sets, achieve detection effectiveness, and support explainability. The results show that the framework techniques implementing our approach outperform the baseline conformance checking-based techniques while maintaining the explainable nature of conformance checking. We also provide an explanation of why existing conformance checking-based techniques may be ineffective. Finally, the results indicate that detection effectiveness is not solely dependent on the specific framework technique used, as no one-size-fits-all process mining-based feature extraction approach is suitable for all the synthetic and real-world datasets.

Digital twin development of industrial cyber-physical systems requires modeling, simulation, and monitoring to provide accurate digital replicas mimicking system dynamics. To this aim, modern solutions employ data-driven approaches to capture normal system dynamics using historical data, and anomaly detection using run-time data. However, these approaches are typically process-agnostic and have limited explainability, which negatively impacts trustworthiness. Hence, we propose a novel framework for the digital twin development of industrial cyber-physical systems based on process mining, which connects data-driven and process-based analyses. The framework implements an offline phase that systematically identifies the most suitable process model to capture normal dynamics and simulates faulty dynamics for what-if analysis. The subsequent online phase involves run-time monitoring for anomaly detection. We develop a proof-of-concept application of the framework addressing a water distribution case study that allows physical fault injection. Results highlight the importance of adopting high-quality process models to significantly improve anomaly detection and time performance.

Obstacle avoidance is a fundamental operation for automated driving and its formulation traditionally originates from robotics and decision making control fields. Given the high complexity required to compute an obstacle-free trajectory, this operation is usually demanded to a lower frequency planning layer that provides then a trajectory reference to be followed by a higher frequency control layer. As a result, whenever replanning is needed (for example, due to a new detected obstacle), the control layer must wait for a new planned trajectory to be generated. In this paper, we propose a novel methodology to approach obstacle avoidance already in the control layer, which allows a prompter response. In particular, we show how obstacle avoidance and reference tracking can be integrated, thus with no need to switch among different controllers, based on a null-space based behavioral control approach, implemented in a (possibly nonlinear) model predictive control scheme. We demonstrate practical implementation of the proposed methodology employing two different vehicle dynamic models and in four different (urban and highway) scenarios. Furthermore, we provide a sensitivity analysis to understand how parameters choice affects the automated vehicle behavior.

Abstract Technological advances in embedded systems and the advent of fog computing led to improved quality of service of applications of cyber-physical systems. In fact, the deployment of such applications on powerful and heterogeneous embedded systems, such as multiprocessors system-on-chips (MPSoCs), allows them to meet latency requirements and real-time operation. Highly relevant to the industry and our reference case-study, the challenging field of nuclear fusion deploys the aforementioned applications, involving high-frequency control with hard real-time and safety constraints. The use of fog computing and MPSoCs is promising to achieve safety, low latency, and timeliness of such control. Indeed, on one hand, applications designed according to fog computing distribute computation across hierarchically organized and geographically distributed edge devices, enabling timely anomaly detection during high-frequency sampling of time series, and, on the other hand, MPSoCs allow leveraging fog computing and integrating monitoring by deploying tasks on a flexible platform suited for mixed-criticality software, leading to so-called mixed criticality systems (MCSs). However, the integration of such software on the same MPSoC opens challenges related to predictability and reliability guarantees, as tasks interfering with each other when accessing the same shared MPSoC resources may introduce non-deterministic latency, possibly leading to failures on account of deadline overruns. Addressing the design, deployment, and evaluation of MCSs on MPSoCs, we propose a model-based system development process that facilitates the integration of real-time and monitoring software on the same platform by means of a formal notation for modeling the design and deployment of MPSoCs. The proposed notation allows developers to leverage embedded hypervisors for monitoring real-time applications and guaranteeing predictability by isolation of hardware resources. Providing evidence of the feasibility of our system development process and evaluating the industry-relevant class of nuclear fusion applications, we experiment with a safety-critical case-study in the context of the ITER nuclear fusion reactor. Our experimentation involves the design and evaluation of several prototypes deployed as MCSs on a virtualized MPSoC, showing that deployment choices linked to the monitor placement and virtualization configurations (e.g., resource allocation, partitioning, and scheduling policies) can significantly impact the predictability of MCSs in terms of Worst-Case Execution Times and other related metrics.

Modern cyber-physical systems based on the Industrial Internet of Things (IIoT) can be highly distributed and heterogeneous, and that increases the risk of failures due to misbehavior of interconnected components, or other interaction anomalies. In this paper, we introduce a conceptual architecture for IIoT anomaly detection based on the paradigms of Digital Twins (DT) and Autonomic Computing (AC), and we test it through a proof-of-concept of industrial relevance. The architecture is derived from the current state-of-the-art in DT research and leverages on the MAPE-K feedback loop of AC in order to monitor, analyze, plan, and execute appropriate reconfiguration or mitigation strategies based on the detected deviation from prescriptive behavior stored as shared knowledge. We demonstrate the approach and discuss results by using a reference operational scenario of adequate complexity and criticality within the European Railway Traffic Management System.

Digital data has become a key part of everyday life: people manage increasingly large and disparate collections of photos, documents, media, etc. But what happens after death? How can users select and prepare what data to leave behind before their eventual death? To explore how to support users, we first ran an ideation workshop to generate design ideas; then, we created a design workbook with 12 speculative concepts that explore diverging approaches and perspectives. We elicited reactions to the concepts from 20 participants (18-81, varied occupations). We found that participants anticipated different types of motivation at different life stages, wished for tools to feel personal and intimate, and preferred individual control on their post-death self-representation. They also found comprehensive data replicas creepy and saw smart assistants as potential aides for suggesting meaningful data. Based on the results, we discuss key directions for designing more personalized and respectful death-preparation tools.

The advent of cloud platforms and mobile devices has complicated personal data management and decisions about what data to keep or discard. To explore how to help people curate their data, we designed Data Dashboard, a prototype system that provides: 1) a centralized overview of data from across platforms and devices, 2) customizable filters for sorting through many types of data. We evaluated the prototype with 18 participants. Building on top of previous literature, we use the concept of data boundaries (the idea of invisible but important separations across data) to explain participants' reactions to the prototype. We show that centralizing data in a single management system blurs boundaries and requires safety guarantees. Customization options, instead, uphold boundaries and reinforce user control. We discuss how to use these results for integrating data boundaries in the design of new tools, rethinking the language of personal data, and envisioning a post-cloud future.

Today, most technology users have to deal with a growing amount of personal data across many devices and online platforms. There is a growing need for tools that can help people decide more intentionally what data to keep or discard. We created five design concepts in the form of video prototypes to probe on alternative design strategies for supporting users. Automation, aggressiveness, and temporality were key dimensions we explored. We conducted interviews with 16 participants using the concepts as a starting point for discussion. Participants had a range of reactions: some wanted to retain full control over keeping and discarding decisions, while others welcomed more automatic tools. We identify common ground in the need for a contextual and nuanced approach in design. We use these results to outline and reflect on possible future design directions for personalization, automation, new keeping or discarding actions, and privacy.

Digital data, from texts to files and mobile applications, has become a pervasive component of our society. With seemingly unlimited storage in the cloud at their disposal, how do people approach data preservation, deciding what to keep and discard? We interviewed 23 participants with diverse backgrounds, asking them about their perceived digital data: what "stuff" they kept through the years, why, how they used it, and what they considered important. In an iterative analysis process, we uncovered a spectrum of tendencies that drive preservation strategies, with two extremes: hoarding (where participants accumulated large amounts of data, even if considered of little value) and minimalism (where they kept as little as possible, regularly cleaning their data). We contrast and compare the two extremes of the spectrum, characterize their nuanced nature, and discuss how our categorization compares to previously reported behaviors such as filing and piling, email cleaners and keepers. We conclude with broad implications for shaping technology.

Users must manage frequent software and operating system upgrades across multiple computing devices. While current research focuses primarily on the security aspect, we investigate the user's perspective of upgrading software. Our first study (n=65) found that users delay major upgrades by an average of 80 days. We then ran a field study (n=14), beginning with in-depth observations during an operating system upgrade, followed by a four-week diary study. Very few participants prepared for upgrades (e.g., backing up files), and over half had negative reactions to the upgrade process and other changes (e.g., bugs, lost settings, unwanted features). During the upgrade process, waiting times were too long, feedback was confusing or misleading, and few had clear mental models of what was happening. Users almost never mentioned security as a concern or reason for upgrading. By contrast, interviews (n=3) with technical staff responsible for one organization's upgrades focused only on security and licensing, not user interface changes. We conclude with recommendations to improve the user's upgrade experience.