Intelligent User Interfaces (IUIs) rely on user metrics to infer user characteristics and adapt interaction accordingly, yet these metrics are often specified in an implementation-dependent manner and become tightly coupled to a particular application context and technology stack. This paper proposes a model-driven abstraction process to specify user metrics independently of the underlying programming language, enabling their integration into Model-Driven Development (MDD) workflows. Building on a curated set of automatic user metrics for knowledge, skills, and goals, we describe how metric parameters can be expressed using conceptual user interface primitives and context constructs. We instantiate the approach with UsiXML and illustrate, through a practical scenario, how abstract metric values can drive systematic UI adaptations at the model level. To evaluate the feasibility of the proposal, we conducted a preliminary expert-based validation with three MDD specialists who evaluated the operationalization of the proposed metrics in UsiXML, WebRatio, and Integranova languages. Results indicate that most metrics can be supported across tools, while time-dependent metrics reveal limitations in environments lacking explicit temporal primitives.
Consent is a cornerstone of lawful and ethical personal data processing in modern information systems, and all main data protection regulations, such as the General Data Protection Regulation (GDPR), require that valid consent be obtained from data subjects before their personal data is processed. Yet, existing privacy-aware modelling approaches offer limited support for explicitly representing consent-related concepts. To address this gap, we present ConsentML, a modelling language tailored for the formal and visual representation of consent and its interactions among key stakeholders. The language supports the engineering of consent-centric requirements by aligning legal, organisational, and technical perspectives. A case study in the financial domain illustrates its applicability in modelling dynamic consent flows, enforcing purpose limitation, and strengthening accountability across organisational boundaries. The results demonstrate that ConsentML facilitates stakeholder communication through visual abstractions, supports compliance-by-design, and enhances the automation of consent management within complex, adaptive data ecosystems.
Territorial development is driven by complex interactions among social, environmental, demographic and other thematic factors. In well-being and age-friendly planning, decision-makers increasingly need to interpret heterogeneous multi-theme spatio-temporal indicators. However, existing assessments are often theme-specific and provide limited support for coherent longitudinal comparison across datasets with mismatched spatial and temporal granularities. This paper introduces the concept of a territorial trajectory as a modeling abstraction that represents a territory as an ordered sequence of territorial states. Each state is defined by explicit spatial, temporal and thematic scopes and associated indicators, enabling heterogeneous datasets to be organized and compared within a unified representation. Building on this model, we develop a visual analytics prototype that supports interactive exploration and comparison of trajectories across themes, territories and time periods. We illustrate the approach using real-world datasets and show how scope-explicit trajectory construction improves the interpretability of multi-theme indicators for territorial analysis and policymaking.
Object-centric processes involve a substantial number of object instances that may be in different states during runtime. In addition, there are numerous dependencies between the states of these object instances. Inter-object constraints can organise these complex, nested dependencies by documenting and controlling them using a higher-level process. To examine the correct runtime behaviour of such process instances, a comparison is made between the event log and the defined inter-object constraints. Conformance checking is thereby used to determine the fitness value. Identifying deviations from these dependencies is crucial for verifying the correct execution of the entire process. This paper proposes a methodology for conformance checking of inter-object constraints based on linear temporal logic rules. The developed approach is evaluated in three different scenarios. The findings demonstrate that the proposed approach constitutes a reliable method for the conformance checking of inter-object constraints.
Driven by Europe’s ageing building stock and the EU’s sustainability targets, renovation of existing buildings has become increasingly important. Building renovation typically comprises three types of activities (operations facility management, condition assessment monitoring, and transformation planning), each involving key decisions that can benefit from advanced decision support systems such as digital twins (DTs). DTs carry the promise of improved decision-making about, as well as the monitoring and understanding of, the twinned entity. This paper investigates how DTs can support, and add value to, the decision-making involved in building renovation and how the added value of these DTs can be assessed and (ideally be) quantified. In doing so, this paper focuses primarily on operations facility management-related activities. By connecting use cases with quantification methods, this paper suggests pathways to evaluate and prioritise DT investments in the context of building renovation.
Manufacturing as a Service (MaaS) is a business and operational model where manufacturing capabilities are offered by their providers to consumers on demand as services. It allows for more efficient utilization of the manufacturing capabilities though leading to increasing complexity of distributed IT systems. The distributed nature of MaaS leads to additional information security risks. Collaboration among the parties involved is required to provide secure MaaS and sharing of security intelligence is highly desirable to ensure resilience. Ontology is a major enabler of secure MaaS by providing a formal, shared understanding of assets, threats, vulnerabilities, and risks and it supports consistent security reasoning across distributed manufacturing systems. The objective of this paper is to propose a Manufacturing as a Service Security Ontology (MSO). The ontology is designed on the basis of existing security ontologies and incorporates MaaS specific entities. It focuses on knowledge sharing and provisioning suitable security solutions for the MaaS stakeholders. The MaaS specific entities are determined on the basis of literature analysis and the survey of the MaaS stakeholders.
The increasing connectivity of the Industrial Internet of Things (IIoT) has transformed security compliance into a major challenge. Manufacturers currently face a disparate set of regulatory and technical requirements, leading to redundant compliance efforts. This paper addresses this inefficiency by developing a Unified Security Requirement Catalog. We consolidate security controls from heterogeneous frameworks (specifically IEC 62443, OWASP ISVS, the Cyber Resilience Act (TR 03183-01), and BSI C5:2020) into a single, actionable mapping. This catalog distinguishes itself by linking technical verification steps directly to high-level regulatory obligations. The utility of the framework was demonstrated through a prototypical implementation and validated via expert interviews. Feedback confirms the framework’s relevance and structural utility, offering a scalable solution for manufacturers aiming to optimize their compliance strategies across multiple standards.
Water supply networks are complex critical infrastructures, vulnerable to operational disruptions that can compromise service continuity and safety. Ensuring reliable operation requires not only detecting anomalies, but also understanding their evolution and impact on the network. Current approaches tend to focus on detecting specific anomalies, without providing a holistic view of incidents or explaining which parts are affected. This paper proposes a multi-stage method for detecting and characterising incidents in operational data. First, specific anomalies are identified in the data records. They are then grouped into temporal sequences that indicate possible incidents. At the same time, the original data is organised into homogeneous clusters with low entropy. Next, clusters and sequences of anomalies are integrated to precisely delimit the records involved in each incident. Finally, the original set is analysed to identify the characteristics that explain the anomalous behaviours, providing interpretative information on the factors affecting the infrastructure. The method was implemented in a prototype that considers a hybrid set of unsupervised algorithms and proprietary methods. Validation with real data from a drinking water distribution network in a city in south-eastern Spain demonstrates the system’s ability to isolate complete incidents and accurately identify the affected components. The method is domain-agnostic and can be applied to any critical infrastructure, requiring only timestamped multivariate records. This method allows for a practical and explainable characterisation of events, overcomes the limitations of traditional anomaly detection methods, and enables accurate diagnosis and rapid remediation.
Sequential recommendation models excel at capturing the temporal dynamics of user behavior and are widely used in domains such as e-commerce and media streaming. However, their reliance on learned interaction patterns makes them especially vulnerable to data poisoning attacks, where adversaries craft and inject malicious profiles to distort recommendations. Despite this risk, the security aspects of sequential recommenders remain largely underexplored, particularly in realistic scenarios. To address this lack, we propose the first cross-domain black-box poisoning attack for sequential recommenders. Our method transfers real user sequences from a source domain so as to craft realistic adversarial profiles for the target system. A recency-aware autoencoder generates user embeddings that capture influential interactions, while an SD-SAC reinforcement learning agent selects which profiles to inject, using a surrogate recommendation model, i.e., without accessing the target system. Experiments on MovieLens-100K (source) and a sampled version of Netflix Prize dataset (target) show that our method outperforms four strong state-of-the-art baselines, achieving up to a 60
Data reduction plays a central role in predictive data analysis, particularly in regression tasks, where increasing data volume and noise levels can adversely affect three key objectives: predictive performance, model interpretability, and computational efficiency. Selecting appropriate data reduction methods in such settings requires balancing these competing objectives, yet systematic and reproducible support for making such choices remains limited. This paper suggests a context-aware, multi-objective framework for data reduction method selection for regression analysis. Given a dataset characterized by its technical properties and a user-defined priority ordering over the considered objectives, the framework empirically characterizes candidate reduction methods, identifies Pareto-efficient trade-offs, and produces ranked recommendations using a lexicographic selection strategy consistent with the specified priorities. The framework is evaluated on real-world regression datasets with publicly available analytical pipelines containing explicitly implemented data reduction choices. The results show that the proposed approach frequently reproduces practitioner-selected reduction strategies under comparable priorities, while also exposing meaningful alternative trade-offs when different objectives are emphasized. These findings indicate that context-aware, preference-aware selection can provide effective and transparent support for data reduction decisions in regression-oriented analytical pipelines.
Legal information systems face critical challenges safeguarding sensitive client data while navigating multi-regulatory professional workflows. Current Legal Information systems employ uniform calendar-based retention policies that contradict GDPR data minimisation mandates, unnecessarily exposing client information across multi-stage legal transactions. This research proposes the Stage-Based Contextual Data minimisation (SB-CDM) framework, a progressive minimisation methodology for legal services that operates at the individual data-element level rather than the category level, reconciling five overlapping regulatory frameworks through automated precedence hierarchies and trigger-based stage recognition. Comparative evaluation with the current uniform 7-year retention baseline approach reveals a reduction of 49
