Temporal planning and scheduling often rely on constraint models to reason about activity durations. Simple Temporal Networks with Uncertainty (STNUs) address cases where some contingent durations are outside the control of the executing agent. The Multiple Interdependent STNU (MISTNU) model extends this setting to multi-agent systems by representing shared activities whose duration is decided by one agent and imposed on others. While controllability properties for MISTNUs have been defined, existing repair methods rely on a centralized SMT-based brute-force approach. This paper introduces a linear-constraint characterization of all negative cycles responsible for uncontrollability. Based on this formulation, we propose both a more efficient centralized linear-programming repair method and a distributed constraint reasoning approach that treats inconsistent cycles as inter-agent constraints. We experimentally compare several Distributed Constraint Optimization Problem (DCOP) solvers against the centralized baseline in terms of efficiency and repair quality.

Fairness has recently emerged as a challenging topic in many areas of computer science, as it is related to algorithms supporting decision-making, experimental research, and information access and processing. As (decision-intensive) business processes are inherently using information to reach their goals, their fairness possibly depends on the kind of information they are allowed to access. In this paper, we deal with this aspect and propose some criteria to consider when conceptually specifying business activities and their related information seamlessly through a recently proposed approach based on the concept of Activity View. More specifically, we distinguish equality and equity as two aspects of fairness and discuss how to enforce them in business process design. Their expression according to the specification of Activity Views is formally proposed and discussed in the paper.

Simple temporal networks with uncertainty (STNUs) have achieved wide attention and are the basis of many applications requiring the representation of temporal constraints and checking whether they are conflicting. Dynamic controllability is currently the most studied notion to check whether a system can be controlled without violating temporal constraints despite uncertainties. However, dynamic controllability assumes that the actual duration of a contingent activity is known only when the end event of that activity occurs. The recently introduced notion of agile controllability considers the case where this duration is known earlier, leading to a more relaxed notion of temporal feasibility. We extend the definition of STNUs to STNUOs (Simple Temporal Networks with Uncertainty and Oracles) to represent the point in time at which information about a contingent duration is available. We formally define agile controllability as a generalization of dynamic controllability considering the timepoints of information availability. We propose a set of constraint propagation rules for STNUOs, leading to an algorithm for checking agile controllability. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

The recent COVID-19 pandemic outbreak has demonstrated all the limitations of modern healthcare information systems in preventing and controlling pandemics, especially following an unexpected event. Existing approaches often fail to integrate real-time data and adaptive learning mechanisms, leading to inefficient response strategies and resource allocation challenges. To address this gap, in this paper, we propose PROTECTION, an innovative data-centric process-modeling-managing-and-mining framework for pandemic control and prevention that is based on the new paradigm that we name Knowledge-, Decision- and Data-Intensive (KDDI) processes. PROTECTION adopts Business Process Model and Notation (BPMN) as a standardized approach to model and manage complex healthcare workflows, enhancing interoperability and formal process representation. PROTECTION introduces a structured methodology that integrates Big Data Analytics, Process Mining and Adaptive Learning Mechanisms to dynamically update healthcare processes in response to evolving pandemic conditions. The framework enables real-time process optimization, predictive analytics for outbreak detection, and automated decision support for healthcare. Through case studies and experimental validation, we demonstrate how PROTECTION can effectively deal with the complex domain of pandemic control and prevention.

Dynamic controllability is currently regarded as the most adequate notion for checking the temporal correctness of business processes with temporal constraints when a process model includes uncontrollable activities whose duration is revealed at the time of activity completion. However, dynamic controllability cannot take advantage when an actual duration is revealed earlier, leading to unnecessary strict checks for temporal correctness. We propose a novel notion of agile controllability, which takes into account that uncontrollable durations are revealed earlier and that in a viable execution strategy, a time point may depend on time points whose value is known earlier. We formalize the notion of agile controllability and present an effective checking procedure evaluated by a software implementation within a publicly available modeling and checking software tool.

The Business Process Modeling Notation (BPMN) is a diagrammatical notation to describe complex process models, and it can be used as a common language among stakeholders. These stakeholders could intervene in the development of the processes, also in an agile environment. Thus, the same process could evolve through different versions over time. BPMN has been already adopted in the healthcare domain, where designing health care processes is fundamental for delivering optimal and efficient services to patients without overburdening health care professionals. Adapting and updating BPMN processes within an agile development is paramount in the rapidly evolving healthcare domain. The challenges of migrating a process to its revised versions have been discussed since the introduction of BPMN. However, there is a lack of migration policies that consider compensation strategies when migrating to a revised version at runtime, combined with healthcare- related migration risk classes. In this paper, we propose a methodological framework that includes migration strategies to adopt when the user is already running the process in a previous version. We propose compen-satory strategies to integrate the novelties of the revised process based on the actual users' completion status, with a specific focus on the addition, modification, and removal of tasks. Moreover, the proposed framework includes a color-coded risk classification system encapsulating migration risks and potential impact on patients. This system provides a visual and intuitive way to understand potential risks, enabling clinicians to make informed decisions about the migration strategy. Finally, we showed an application of the proposed framework in a real-world scenario, that is, through an ERAS-inspired prehabilitation program for pancreatic surgery currently developed at the Verona Pancreas Institute.

In this paper, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework and on a multi-temporal relational model. Different features are proposed for the Observation Window (OW), where we observe predictive data, for the Waiting Window (WW), and for the Prediction Window (PW), where the predicted event occurs. We then discuss the concept of approximation for such APFDs, introduce two new error measures. We prove that the problem of deriving APFDs is intractable. Moreover, we discuss some preliminary results in deriving APFDs from real clinical data using MIMIC III dataset, related to patients from Intensive Care Units.

A Conditional Simple Temporal Network with Uncertainty (CSTNU) models temporal constraint satisfaction problems in which the environment sets uncontrollable timepoints and conditions. The executor observes and reacts to such uncontrollable assignments as time advances with the CSTNU execution. However, there exist scenarios in which the occurrence of some future timepoints must be fixed as soon as the execution starts. We call these timepoints parameters. For a correct execution, parameters must assume values that guarantee the possibility of satisfying all temporal constraints, whatever the environment decides the execution time for uncontrollable timepoints and the truth value of conditions, i.e., dynamic controllability (DC). Here, we formalize the extension of the CSTNU with parameters. Furthermore, we define a set of rules to check the DC of such extended CSTNU. These rules additionally solve the problem inverse to checking DC: computing restrictions on parameter values that yield DC guarantees. The proposed rules can be composed into a sound and complete procedure.

Modeling and reasoning over business processes require enterprises to manage and integrate large amounts of information. Despite process designers and engineers may benefit from a unified view of process and data models, integrating these two perspectives is challenging, especially when considering conceptual models. In this article, we provide a uniform formal representation of a process model, the schema of a related database, and the data operations connecting them. Then, we show how we can use such a formal representation to identify interesting information during the integrated conceptual modeling and analysis of processes and related databases, from a process (re-)design and improvement perspective. Finally, we discuss the evaluation of the proposed approach through a controlled experiment and a proof-of-concept implementation that considers both relational and XML database technologies.

Managing temporal process constraints in modularized processes is an important task, both during the design, as it allows the reuse of temporal (child) process models, and during the checking of temporal properties of processes, as it avoids the necessity of "unfolding'' child processes within the main process model. Taking into account the capability of providing modular solutions, modeling and checking temporal features of processes is still an open problem in the context of process-aware information systems. In this paper, we present and discuss a novel approach to represent flexible temporal constraints in modularized time-aware BPMN process models. To support temporal flexibility, allowed task durations are represented through guarded ranges that allow a limited (guarded) restriction of task durations during process execution if it is necessary to guarantee the satisfaction of all temporal constraints. We, then, propose how to derive a compact representation of the overall temporal behavior of such time-aware BPMN models. Such compact representation of child processes allows us to check the dynamic controllability (DC) of a parent timeaware process model without "unfolding'' the child process models. Dynamic controllability guarantees that process models can have process instances (i.e., executions) satisfying all the temporal constraints for any possible combination of allowed durations of tasks and child processes. Possible approaches for even more flexibility by solving some kinds of DC violations are then introduced. We use a real process model from a healthcare domain as a motivating example, and we also present a proof-of-concept prototype confirming the concrete applicability of the solutions we propose, followed by an experimental evaluation. & COPY; 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

A Flexible Simple Temporal Network with Uncertainty (FTNU) represents temporal constraints between time-points. Time-points are variables that must be set (executed) satisfying all the constraints. Some time-points are contingent. It means that they are set by the environment and only observed by the system executing the network. The ranges representing temporal constraints associated with contingent time-points (guarded ranges) can be shrunk during execution only to some extent to have more flexibility in the execution of the network. Subsets of time-points/constraints may be executed/considered in different contexts according to some observed conditions. The main issue here consists of determining whether all the time-points, under the control of the system, are executable in a way that all the specified constraints are satisfied for any possible occurrence of contingent time-points and any possible context. Such property is called controllability. Even though an algorithm was proposed for checking the controllability of such networks, we show that such an algorithm has a limit. Indeed, it does not determine the right bounds for guarded links, and, therefore, it doesn’t permit the system to exploit the potential flexibility of the network. We then propose a new constraint-propagation algorithm for checking controllability, prove that such a new algorithm determines the right guarded ranges, and it is sound-and-complete. Thus, it can be used also for executing the network, by leveraging its flexibility.

This paper proposes a comprehensive approach to conceptual log file modelling. It is based on the UML (Unified Modelling Language) class diagrams, which notation was extended to represent the main functionality of the nested structure of JSON (JavaScript Object Notation) log files. An implementation of the approach is illustrated in two domains: banking applications and networking. This study is a first step towards the sound development of a centralized log files management system. We use the ECS (Elastic Common Schema) initiative as a starting point. We also present the developed plugin to the StarUML software, which allows modelling logs in both UML and JSON formats.

This paper aims to predict the risk of Acute Kidney Injury (AKI) in intensive care units (ICUs) using machine learning techniques and statistical approaches. The data used in the study are derived from Medical Information Mart for Intensive Care (MIMIC) III, which is a freely accessible database of de-identified ICU-related data. The paper focuses on two different phases. The first one consists of a scrupulous phase of extraction and transformation of MIMIC data to retrieve all the criteria specified in the Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guideline definition of AKI. The main features included are demographics, medications, comorbidities, charted vital signs, and laboratory events. In the second phase, we used several different techniques already used to predict AKI, and we also added a complex temporal feature, called Trend-Event Feature (TE-F). The prediction was performed using a rolling observational window design that includes the data collection window (length: 1 to 6 days) and the prediction window (7 days). The Gradient Boosting Decision Trees (GBDT) method was used, with different groups of features, to predict the risk of AKI. To evaluate the GBDT performances, we used the area under the ROC curve (AUROC), Recall, Precision, and F-score measures. We observed that lab parameters contribute the most to the prediction of the AKI risk. Moreover, adding TE-Fs to different groups of features leads to better performance results.

The proceedings contain 15 papers. The topics discussed include: simple temporal networks: a practical foundation for temporal representation and reasoning;extreme-scale model-based time series management with ModelarDB;kernel machines in time;temporal big data analytics: new frontiers for big data analytics research;model checking of stream processing pipelines;investigation of database models for evolving graphs;interval temporal random forests with an application to COVID-19 diagnosis;Pspace-completeness of the temporal logic of sub-intervals and suffixes;deciding FO-rewritability of ontology-mediated queries in linear temporal logic;a neuro-symbolic approach to structured event recognition;efficient anytime computation and execution of decoupled robustness envelopes for temporal plans;and achieving a sequenced, relational query language with log-segmented timestamps.

A Simple Temporal Network (STN) consists of time points modeling temporal events and constraints modeling the minimal and maximal temporal distance between them. A Simple Temporal Network with Decisions (STND) extends an STN to model temporal plans with decisions. STNDs label time points and constraints by conjunctions of literals saying for which scenarios (i.e., complete truth value assignments to the propositions) they are relevant. In this paper, we deal with the use of STNDs for modeling and synthesizing execution strategies. We propose an incremental hybrid SAT-based consistency checking algorithm for STNDs that is faster than the one previously proposed and allows for the synthesis of all consistent scenarios and related early execution schedules (offline temporal planning). We carry out an experimental evaluation with Kappa, a tool that we developed for STNDs. We also show that any STND can be easily translated into a disjunctive temporal network and vice versa.