Recent advances in deep learning have improved multivariate time series (MTS) classification and regression by capturing complex patterns, but their lack of transparency hinders decision-making. Explainable AI (XAI) methods offer partial insights, yet often fall short of conveying the full decision space. Counterfactual Explanations (CE) provide a promising alternative, but current approaches typically prioritize either accuracy, proximity or sparsity -- rarely all -- limiting their practical value. To address this, we propose CONFETTI, a novel multi-objective CE method for MTS. CONFETTI identifies key MTS subsequences, locates a counterfactual target, and optimally modifies the time series to balance prediction confidence, proximity and sparsity. This method provides actionable insights with minimal changes, improving interpretability, and decision support. CONFETTI is evaluated on seven MTS datasets from the UEA archive, demonstrating its effectiveness in various domains. CONFETTI consistently outperforms state-of-the-art CE methods in its optimization objectives, and in six other metrics from the literature, achieving ≥ 10% higher confidence while improving sparsity in ≥ 40%.

In recent years, sustainability has emerged as a major priority for businesses across various industries, and the manufacturing sector is no exception. Production and maintenance processes now need to be economically profitable while also adopting practices that adhere to the principles of environmental integrity and social responsibility. This article explores an innovative approach aimed at optimizing maintenance scheduling from an economic perspective (considering maintenance, breakdown, downtime costs), an environmental perspective (considering the carbon footprint produced during production) and a social perspective (considering the fatigue experienced by technicians during maintenance activities). To the best of our knowledge, this is the first study to propose a manufacturing scheduling approach that considers all three pillars of sustainability. Another significant contribution of this research is the innovative way in which the optimization problem is addressed. We propose an evolutionary multi-objective multi-agent Deep Q-network-based approach, where multiple agents explore the preference space to maximize the hypervolume of these sustainable objectives. Our methodology uses industrially representative data that incorporate realistic machine degradation signals, carbon intensity indicators, and technician constraints. The results demonstrate the trade-offs between these objectives when compared to traditional maintenance policies such as corrective and condition-based maintenance, as well as different Deep Q-network policies trained with various preferences. Our approach demonstrates superior performance compared to both baselines. Specifically, we observe an 11.6% improvement in hypervolume over Deep Q-network and an 18.9% improvement over Proximal Policy Optimization, resulting in significantly increased profitability within the system.

Maintenance planning and scheduling are an essential part of manufacturing companies to prevent machine breakdowns and increase machine uptime, along with production efficiency. One of the biggest challenges is to effectively address uncertainty (e.g., unexpected machine failures, variable time to repair). Multiple approaches have been used to solve the maintenance scheduling problem, including dispatching rules (DR), metaheuristics and simheuristics, or most recently reinforcement learning (RL). However, to the best of our knowledge, no study has ever studied to what extent these techniques are effective when faced with different levels of uncertainty. To overcome this gap in research, this paper presents an approach by analyzing the impact of categorized levels of uncertainty, specifically high and low, on the failure distribution and time to repair. Upon the formalization of the maintenance scheduling problem, the experiments conducted are performed in simulated scenarios with different degrees of uncertainty, and also considering a real-life manufacturing use case. The results indicate that rescheduling based on a genetic algorithm (GA) simheuristic outperforms RL and DR in terms of total machine uptime, but not in terms of the mean time to repair when configured with high re-optimization frequencies (i.e., hourly re-optimization), but rapidly underperforms when the re-optimization frequency decreases. Furthermore, our study demonstrates that GA-simheuristic is highly computationally demanding compared to RL and rule-based policies.

Ontology search and ranking are key building blocks to establish and reuse shared conceptualizations of domain knowledge on the Web. However, the effectiveness of proposed ontology ranking models is difficult to compare since these are often evaluated on diverse datasets that are limited by their static nature and scale. In this paper, we first introduce the LOVBench dataset as a benchmark for ontology term ranking. With inferred relevance judgments for more than 7000 queries, LOVBench is large enough to perform a comparison study using learning to rank (LTR) with complex ontology ranking models. Instead of relying on relevance judgments from a few experts, we consider implicit feedback from many actual users collected from the Linked Open Vocabularies (LOV) platform. Our approach further enables continuous updates of the benchmark, capturing the evolution of ontologies’ relevance in an ever-changing data community. Second, we compare the performance of several feature configurations from the literature using LOVBench in LTR settings and discuss the results in the context of the observed real-world user behavior. Our experimental results show that feature configurations which are (i) well-suited to the user behavior, (ii) cover all features types, and (iii) consider decomposition of features can significantly improve the ranking performance.

Fuzzy AHP is today one of the most used Multiple Criteria Decision-Making (MCDM) techniques. The main argument to introduce fuzzy set theory within AHP lies in its ability to handle uncertainty and vagueness arising from decision makers (when performing pairwise comparisons between a set of criteria/alternatives). As humans usually reason with granular information rather than precise one, such pairwise comparisons may contain some degree of inconsistency that needs to be properly tackled to guarantee the relevance of the result/ranking. Over the last decades, several consistency indexes designed for fuzzy pairwise comparison matrices (FPCMs) were proposed, as will be discussed in this article. However, for some decision theory specialists, it appears that most of these indexes fail to be properly “axiomatically” founded, thus leading to misleading results. To overcome this, a new index, referred to as KCI (Knowledge-based Consistency Index) is introduced in this paper, and later compared with an existing index that is axiomatically well founded. The comparison results show that (i) both indexes perform similarly from a consistency measurement perspective, but (ii) KCI contributes to significantly reduce the computation time, which can save expert's time in some MCDM problems.

The Internet of Things (IoT) is promising to open up opportunities for businesses to offer new services to uncover untapped needs. However, before taking advantage of such opportunities, there are still challenges ahead, one of which is the development of strategies to abstract from the heterogeneity of APIs that shape today's IoT. It is becoming increasingly complex for developers and smart connected objects to efficiently discover, parse, aggregate and process data from disparate information systems, as different protocols, data models, and serializations for APIs exist on the market. Standards play an indisputable role in reducing such a complexity, but will not solve all problems related to interoperability. For example, it will remain a permanent need to help and guide data/service providers to efficiently describe the data/services they would like to expose to the IoT. This paper presents PROFICIENT, a productivity tool that fulfills this need, which is showcased and evaluated considering recent open messaging standards and a smart parking scenario.

The Industrial Internet should provide means to create ad hoc and loosely coupled information flows between objects, users, services, and business domain systems. However, today's technologies and products often feed `vertical silos' (e.g., vertical/siloed apps), which inevitably result in multiple and non-interoperable systems. Standardization will play an ever-increasing part in enabling information to flow between such vertically-oriented closed systems. This paper presents recent IoT messaging standards, notably O-MI (Open Messaging Interface) and O-DF (Open Data Format), whose initial requirements were defined for enhanced collaboration and interoperability in product lifecycle management. The performance of those standards is evaluated in terms of efficiency ratio, defined as the percentage of payload over traffic load. A first analytical model of the efficiency ratio based on the required/basic standard specifications is then proposed. A smart maintenance use case relying on the first version of the standard reference implementation is developed, based on which our analytical model is applied to evaluate the degree of deviation (w.r.t. the standard specifications) of this reference implementation.

By connecting devices, people, vehicles and infrastructures everywhere in a city, governments and their partners can improve community wellbeing and other economic and financial aspects (e.g., cost and energy savings). Nonetheless, smart cities are complex ecosystems that comprise many different stakeholders (network operators, managed service providers, logistic centers...) who must work together to provide the best services and unlock the commercial potential of the IoT. This is one of the major challenges that faces today's smart city movement, and more generally the IoT as a whole. Indeed, while new smart connected objects hit the market every day, they mostly feed "vertical silos" (e.g., vertical apps, siloed apps...) that are closed to the rest of the IoT, thus hampering developers to produce new added value across multiple platforms. Within this context, the contribution of this paper is twofold: (i) present the EU vision and ongoing activities to overcome the problem of vertical silos; (ii) introduce recent IoT standards used as part of a recent Horizon 2020 IoT project to address this problem. The implementation of those standards for enhanced sporting event management in a smart city/government context (FIFA World Cup 2022) is developed, presented, and evaluated as a proof-of-concept.

Businesses are increasingly using their enterprise data for strategic decision-making activities. In fact, infor-mation, derived from data, has become one of the most important tools for businesses to gain competitive edge. Data quality assessment has become a hot topic in numerous sectors and considerable research has been carried out in this respect, although most of the existing frameworks often need to be adapted with respect to the use case needs and features. Within this context, this paper develops a methodology for assessing the quality of enterprises ’ daily maintenance reporting, relying both on an existing data quality framework and on a Multi-Criteria Decision Making (MCDM) technique. Our methodology is applied in cooperation with a Finnish multinational company in order to evaluate and rank different company sites/office branches (carry-ing out maintenance activities) according to the quality of their data reporting. Based on this evaluation, the industrial partner wants to establish new action plans for enhanced reporting practices. 1

The Industrial Internet promises to radically change and improve many industry's daily business activities, from simple data collection and processing to context-driven, intelligent and pro-active support of workers' everyday tasks and life. The present paper first provides insight into a typical industrial internet application architecture, then it highlights one fundamental arising contradiction: “Who owns the data is often not capable of analyzing it”. This statement is explained by imaging a visionary data supply chain that would realize some of the Industrial Internet promises. To concretely implement such a system, recent standards published by The Open Group are presented, where we highlight the characteristics that make them suitable for Industrial Internet applications. Finally, we discuss comparable solutions and concludes with new business use cases.

Building Information Management systems have started to leverage new kinds of digital information infrastructures that integrate activities related to design, budgeting, scheduling, analysis, material management, and human resources. Companies implementing such systems have no other option today than exchanging information among themselves in a proper and efficient way. Industry foundation classes (IFCs) provide useful information structures for such a data sharing. However they do not specify how to capture, exchange and synchronize the information between distinct entities (i.e. information systems like sensors, servers, smart products...) throughout the building lifecycle. Accordingly, this paper investigates existing messaging protocols in order to identify which one is the most appropriate for supporting building lifecycle management; i.e a flexible protocol that provides sufficiently generic communication interfaces. A platform set up on a university campus based on the selected messaging protocol is then presented. Within this context, the research agenda that should be pursued to develop new techniques and algorithms for optimizing the building energy management is announced.

According to our original vision of the Internet of Things (IoT), it should be possible to create ad hoc and loosely coupled information flows between any kinds of products, devices, computers, users, and information systems in general when and as needed. However, this is still challenging to achieve in practice due to the lack of sufficiently generic and standardized interfaces for creating the needed information flows between all devices and systems that the IoT is composed of. The paper presents necessary requirements for such interfaces, as well as proposed interface standards that fulfill those requirements. The paper describes the design principles and provides a high-level description of the proposed standards, followed by real-life implementations that illustrate why such standards are needed and how they are applied.