This paper presents a unified framework for the detection, classification, and preliminary localization of anomalies in water distribution networks using multivariate statistical analysis. The approach, termed SICAMS (Statistical Identification and Classification of Anomalies in Mahalanobis Space), processes heterogeneous pressure and flow sensor data through a whitening transformation to eliminate spatial correlations among measurements. Based on the transformed data, the Hotelling’s T 2 statistic is constructed, enabling the formulation of anomaly detection as a statistical hypothesis test of network conformity to normal operating conditions. It is shown that Hotelling’s T 2 statistic can serve as an integral indicator of the overall “health” of the system, exhibiting correlation with total leakage volume, and thereby enabling approximate estimation of water losses via a regression model. A heuristic algorithm is developed to analyze the T 2 time series and classify detected anomalies into abrupt leaks, incipient leaks, and sensor malfunctions. Furthermore, a coarse leak localization method is proposed, which ranks sensors according to their statistical contribution and employs Laplacian interpolation to approximate the affected region within the network. Application of the proposed framework to the BattLeDIM L-Town benchmark dataset demonstrates high sensitivity and reliability in leak detection, maintaining robust performance even under multiple leaks. These capabilities make the method applicable to real-world operational environments without the need for a calibrated hydraulic model.

Association Rule Mining (ARM) is the task of mining patterns among data features in the form of logical rules, with applications across a myriad of domains. However, high-dimensional datasets often result in an excessive number of rules, increasing execution time and negatively impacting downstream task performance. Managing this rule explosion remains a central challenge in ARM research. To address this, we introduce Aerial+, a novel neurosymbolic ARM method. Aerial+ leverages an under-complete autoencoder to create a neural representation of the data, capturing associations between features. It extracts rules from this neural representation by exploiting the model’s reconstruction mechanism. Extensive evaluations on five datasets against seven baselines demonstrate that Aerial+ achieves state-of-the-art results by learning more concise, high-quality rule sets with full data coverage. When integrated into rule-based interpretable machine learning models, Aerial+ significantly reduces execution time while maintaining or improving accuracy.

Although privacy-preserving mechanisms endeavor to safeguard sensitive information at the attribute level, detected event patterns can still disclose privacy-sensitive knowledge in distributed complex event processing systems (DCEP). Events might not be inherently sensitive, but their aggregation into a pattern could still breach privacy. In this paper, we study in the context of APP-CEP the problem of integrating pattern-level privacy in event-based systems by selective assignment of obfuscation techniques to conceal private information. Compared to state-of-the-art techniques, we seek to enforce privacy independent of the actual events in streams. To support this, we acquire queries and privacy requirements using CEP-like patterns. The protection of privacy is accomplished through generating pattern dependency graphs, leading to dynamically appointing those techniques that have no consequences on detecting other sensitive patterns, as well as non-sensitive patterns required to provide acceptable Quality of Service. Besides, we model the knowledge that might be possessed by potential adversaries to violate privacy and its impacts on the obfuscation procedure. We assessed the performance of APP-CEP in a real-world scenario involving an online retailer’s transactions. Our evaluation results demonstrate that APP-CEP successfully provides a privacy-utility trade-off. Modeling the background knowledge also effectively prevents adversaries from realizing the modifications in the input streams.

In the dynamic landscape of machine learning applications on streaming data, the constant evolution of models and input data complicates optimal model deployment. The static selection of a model risks suboptimal performance as data patterns evolve, while frequent redeployments increase operational costs. This paper proposes a self-adaptive system that autonomously selects interchangeable models for processing streaming data while balancing the tradeoff of performance and redeployment frequency. Inspired by the MAPE-K reference model, our approach utilizes an adaptive model selection control loop to continuously monitor model performance on production and experimental data. "what-if" environments are introduced to collect additional experimental data, simulating production-like scenarios. A selection algorithm that employs two distinct adaptation policies is introduced that strategically plans the selection of the most suitable module for upcoming data. Leveraging a learning-based method, we improve the efficiency of our system by recognizing the patterns of selection eliminating the need for further experimental data collection. Empirical evaluation on an energy forecasting use case spans over 16 years of data demonstrates a substantial reduction in errors up to 34% compared to the best static selection, affirming the proposed framework’s effectiveness. Our findings reveal the potential to discontinue experimental "what-if" analyses with just 12% of historical data, which underlines the practicality of our adaptive strategy on a long-lasting task.

Abstract Conventional digital twins (DT) for critical infrastructures are widely used to model and simulate the system’s state. But fundamental environment changes bring challenges for DT adaptation to new conditions, leading to a progressively decreasing correspondence of the DT to its physical counterpart. This paper introduces the DiTEC system, a Digital Twin for Evolutionary Changes in Water Distribution Networks (WDN). This framework combines novel techniques, including semantic rule learning, graph neural network-based state estimation, and adaptive model selection, to ensure that changes are adequately detected, processed and the DT is updated to the new state. The DiTEC system is tested on the Dutch Oosterbeek region WDN, with results showing the superiority of the approach compared to traditional methods.

Electric Vehicle (EV) charging demand forecasting holds paramount significance in advancing sustainable transportation systems, particularly as electric vehicle adoption surges globally. Accurate predictions of charging demand are instrumental for optimizing charging infrastructure, energy management, and grid stability. By forecasting the demand for charging, stakeholders can effectively distribute resources, plan ahead for peak usage times, and lay out blueprints for the growth of infrastructure. Furthermore, precise forecasting enables the seamless integration of renewable energy sources into transportation, promoting a cleaner and greener future. In this work, challenges in EV charging demand forecasting are addressed, and an innovative framework tailored for large-scale prediction is proposed. The methodology involves generating individual forecasts for multiple charging stations, enabling a comprehensive evaluation of forecasting models across diverse contexts. The potential of global deep learning models to enhance prediction accuracy by capturing shared patterns across time series is explored. These models exhibit remarkable generalization capabilities, proving effective even in forecasting demand at previously unobserved charging stations. The contributions of this research encompass both methodologies and insights, enriching the realm of accurate EV charging demand forecasting. This work bears significance in fostering the integration of electric vehicles into transportation systems, aligning with the trajectory towards sustainable energy solutions.

Digital Twins (DT) facilitate monitoring and reasoning processes in cyber–physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs.

Emerging data driven digital economy and Industry 4.0 transforms all sectors including industry, research and business. It requires new types of the general workforce and specialists that can effectively use digital and data technologies, variety of tools and global infrastructure services, and are capable for personal skills management and self-study. The paper provides an overview of the existing frameworks for competences, skills and qualifications that sets up a baseline for further research and definition of the skills for emerging data driven digital economy. The paper analyses the general profile of the modern agile data driven companies, their culture and required competences. The paper shares the experience of introducing professional and 21stCentury skills in the bachelor and master courses. The project based learning model is proven to be effective in this cases combining the student initiative and self-organisation given the well defined course objectives and reporting requirements.

This paper investigates how to complement Complex Event Processing (CEP) with dynamic quality monitoring mechanisms and support the dynamic integration of suitable sensory data sources. In the proposed approach, queries to detect complex events are annotated with consumer-definable quality policies that are evaluated and used to autonomously assign (or even configure) suitable data sources of the sensing infrastructure. We present and study different forms of expressing quality policies and explore how they affect the process of quality monitoring including different modes of assessing and applying quality-related adaptations. A performance study in an IoT scenario shows that the proposed mechanisms in supporting quality policy monitoring and adaptively selecting suitable data sources succeed in enhancing the acquired quality of results while fulfilling consumers' quality requirements. We show that the quality-based selection of sensor sources also extends the network's lifetime by optimizing the data sources' energy consumption.

Flexible and easily adjustable reasoning mechanisms are essential for rendering sensor and actuator rich indoor environments smart. Constraint-based solutions are a suitable approach for such systems. We propose an approach that allows users to specify the rules for a building's behavior, and uses context information to represent the rules and environment as a dynamic constraint satisfaction problem. The dependency graph data structure allows to find efficiently only the affected parts of the environment, thus minimizing the computational efforts after every event. We evaluate the system on a building implementation as a living lab, and with performance experiments. The testing proves the high efficiency and applicability of the approach for dynamic control of smart environments.

Pervasive context-aware systems base their responses on information about the environment collected from ubiquitous sensors. The inevitable drawback of such systems is that raw data collected from sensors is often noisy, corrupted, and imprecise. Erroneous sensor readings create uncertainties and ambiguous interpretations. Thus creating an interpretation challenge for the context-aware system that needs to reason about possible states of only partially observable subjects. We propose a mechanism for pervasive context-aware systems to process the information gathered from sensors so to obtain knowledge about possible environment states. This includes both the ability to reason about a situation with incomplete knowledge and to cope with erroneous contexts. We present a probabilistic approach to reason about the likelihood of each particular situation, state of a variable, and variable interdependence. The evaluation shows that the proposed approach is applicable to real-time context inference problems.