Over the last two decades, semantic ontologies have been developed to represent manufacturing data across various domains. These ontologies constitute the knowledge base of manufacturing management systems, which primarily focus on optimizing the manufacturing process and improving its resilience. The ontologies developed in the Industry 4.0 domain are heterogeneous, hindering the interoperability of machines, devices, and applications composing manufacturing systems. Consequently, a demand arises for an ontology that provides common vocabularies to represent the data domains inherent to Industry 4.0. A global Industry 4.0 ontology must be easily reusable in different application contexts. This paper presents I40GO: a global ontology tailored to the Industry 4.0 domain. I40GO structures in layers and modules the knowledge represented in the Industry 4.0 most relevant ontologies. The MODDALS methodology is followed to classify knowledge into different layers. This methodology classifies ontology knowledge into common, variant, and application-specific layers following a similar approach to that of Software Product Lines (SPL). I40GO assists ontology engineers in developing domain-specific ontologies for manufacturing systems and enhances interoperability among applications. This work provides an overview of I40GO, emphasizing its development methodology and its modular and layered structure. Furthermore, it demonstrates the reuse of the I40GO ontology within an Industry 4.0 use case-an architecture for context-aware workflow management.

Manufacturing processes of the future will rely on standards for asset interoperability and service orchestration. The Asset Administration Shell (AAS) facilitates information exchange among Industry 4.0 assets, while standardized Business Processes enable workflow execution in manufacturing systems. Combining these technologies provides agility and scalability to manufacturing systems by incorporating asset services within business processes. Service orchestration involves coordinating multiple services, which must be dynamic during runtime to manage unforeseen situations that may arise during the manufacturing process. Context information plays a crucial role in identifying such scenarios and selecting the most suitable devices/services in response, and the Semantic Web accurately represents this information. This paper proposes a context-aware approach for service orchestration using industrial asset services. Our contributions include (1) a component for Context-Aware Service Re-Selection. (2) a domain-specific ontology (DeviceServiceOnt) for Semantic Web-based context representation. And, (3) validation of our proposal in a manufacturing setting where robots are responsible for dispatching and distributing materials within a warehouse. Opportunities for future work are also highlighted, with a primary focus on enhancing workflow dynamicity with context-aware capabilities.

In recent years, mobile devices have witnessed an exceptional surge in web browsing, emerging as the primary information source. Developers, however, struggle to cater to every user’s needs, compelling users to customize web content. Moreover, mobile interactions, including URL typing, scrolling, tapping, and tab-switching, are more cumbersome due to limited screen size, leading to an unpleasant web experience. Web Augmentation offers a solution by enhancing the user experience, reducing interactions, and meeting information needs. We introduce MAWA, a Firefox mobile extension that empowers users to adapt websites. Results demonstrate MAWA’s effectiveness in reducing interactions, battery consumption, and information retrieval time.

The manufacturing industry of the future requires innovative approaches to optimize operational efficiency and adaptability. Integrating context-awareness into workflow management systems has emerged as a promising avenue to enhance efficiency in modern manufacturing processes. This research presents an innovative context-aware workflow management architecture designed to address industry-related challenges and overcome current limitations in the state-of-the-art. The architecture leverages Industry 4.0 standards for asset representation and workflow notation while incorporating a Context Analyzer component for real-time context interpretation. The effectiveness of the proposed solution is demonstrated in a real-world manufacturing setting, specifically in the scenario of collecting work order materials using the Robot Operating System (ROS) technology for robot navigation. The evaluation showcases improvements in task completion rate, resource utilization, and task completion time. These outcomes exemplify the potential benefits of incorporating context-awareness into manufacturing workflows, providing insights for further improvements. Contributions include advancing the understanding of context-aware workflow management, a review of the challenges that cap its adoption in the manufacturing domain, a qualitative comparison of similar approaches, practical implementation of the proposed architecture, evaluation of the context-aware component, and provision of the source code and datasets to the community for future advancement and reproducibility.

The use of mobile devices for web browsing has increased extraordinarily in recent years, becoming the main source of information. Unfortunately, developers cannot meet the needs of all users. As a result, users have been forced to adapt web content to their needs. Additionally, users face more costly interactions than on desktop computers due to the smaller screen size of cell phones. These interactions include URL typing, scrolling, taping, branching and tab-switching. If the number of web resources is large, the activity becomes unpleasant. Furthermore, should the quantity of interactions be extensive, the web experience also becomes unpleasant. Moreover, all these interactions lead to higher costs in terms of phone time and battery life, as well as more costly navigation. Web augmentation enhances the user web experience by adding content from different resources, modifying the original layout, or reducing interactions during web searches, among other benefits. To reduce user interactions and satisfy their information necessities, we propose MAWA, an extension for Firefox mobile that allows users to adapt any web page to their needs through web augmentation techniques. To validate our proposal, an evaluation of the tool has been carried out. The article presents the benefits introduced by MAWA as a response to the inconveniences experienced by users when performing their activities in the mobile browser. The results obtained show that MAWA is able to reduce the number of interactions significantly, as well as to reduce battery consumption and the time needed to obtain the desired information. In addition, users perceive that navigation is less cluttered.

Industries frequently encounter the need to orchestrate services provided by devices as business processes. These industrial business process models need to meet Industry 4.0 (I4.0) specifications to handle unpredictable scenarios in the manufacturing process. Asset Administration Shell (AAS) is considered the cornerstone of interoperability between machines and applications that compose manufacturing systems. AAS facilitates the digitization of physical things (assets) for virtual representation, turning an object into an I4.0 component. This paper investigates the usage of AAS in the context of business process orchestration and a proposal is presented based on those drawbacks. The contributions of this paper are 1) Present an architecture for the management of AAS-based business processes. 2) Introduce an AAS Submodel template that enables the description and registration of the RestServices of an asset. 3) Present a plugin for Camunda Modeler that enables the Service-Discovery mechanism from a chosen AAS repository and maps assets services into BPMN Service-Tasks. And, 4) Outline opportunities for future work between AAS and business process management systems with a primary focus on context-aware capabilities for enhancing the dynamicity of workflows.

Smart Manufacturing Systems (SMS) are software systems that identify opportunities for automating manufacturing operations by using Internet of Things (IoT) devices and services connected to machines. An active challenge of SMS is to satisfy the ever-changing conditions of industries, supply networks, and customer needs. To operate effectively, SMS should be flexible enough to perform automatic or semi-automatic adjustments to manufacturing processes in response to unexpected changes, a feature called context awareness. Recent advances in interpreting context data in the semantic web have permitted SMS to understand the active situation of manufacturing processes. This paper presents a literature analysis of context-aware workflow management approaches in the smart manufacturing domain, with a particular focus on semantic web-based approaches published from 2015 to 2022. A Systematic Literature Review (SLR) methodology was applied to analyze the state-of-the-art via the PICOC method. The contributions of this work are (1) an SLR about context-aware workflow management for smart manufacturing systems focusing on semantic web-based approaches, (2) a systematic taxonomy to break down the approaches in conformity based on content and main workflow management function area, and (3) identification of opportunities for improvement in technical features such as context awareness, use case implementation, tools employed, licensing, security, and scalability. A novel architecture and components are also proposed to address the identified active challenges.

In an era ruled by data and information, engineers need new tools to cope with the increased complexity of industrial operations. New architectural models for industry enable open communication environments, where workflows can play a major role in providing flexible and dynamic interactions between systems. Workflows help engineers maintain precise control over their factory equipment and Information Technology (IT) services, from the initial design stages to plant operations. The current application of workflows departs from the classic business workflows that focus on office automation systems in favor of a manufacturing-oriented approach that involves direct interaction with cyber-physical systems (CPSs) on the shop floor. This paper identifies relevant industry-related challenges that hinder the adoption of workflow technology, which are classified within the context of a cohesive workflow lifecycle. The classification compares the various workflow management solutions and systems used to monitor and execute workflows. These solutions have been developed alongside the Eclipse Arrowhead framework, which provides a common infrastructure for designing systems according to the microservice architectural principles. This paper investigates and compares various solutions for workflow management and execution in light of the associated industrial requirements. Further, it compares various microservice-based approaches and their implementation. The objective is to support industrial stakeholders in their decision-making with regard to choosing among workflow management solutions.

The widespread use of new technologies by students has forced universities to include active methodologies in their pedagogy. This process has accelerated due to the COVID-19 pandemic, introducing innovative changes in pedagogy. This has motivated many lecturers to increase student motivation. The need to keep the students' attention during long and tedious theoretical sessions has motivated this contribution. Furthermore, the need for students to develop their Web search skills and development of individual expertise and participation in a final group process that attempts to transform newly acquired information into a more sophisticated understanding has inspired this contribution. This paper presents the results obtained from the implementation of gamification and Discovery Learning instructional model in the Software Engineering subject of the Computer Science degree in the course 2021/2022. The joint use of gamification and the Discovery Learning instructional model through Webquests has never been studied before. They help students to better acquire the knowledge taught in class. The gap in the combination use of gamification and Discovery Learning instructional model compared to previous studies using one single method show an improvement in academic results, greater motivation on the part of the students, greater creativity and ability to put what they have learned into practice.

Smart Manufacturing Systems (SMS) are software systems that identify opportunities for automating manufacturing operations by using Internet of Things (IoT) devices and services connected to machines. An active challenge of SMS is to satisfy the ever-changing conditions of industries, supply networks, and customer needs. To operate effectively, SMS should be flexible enough to perform automatic or semi-automatic adjustments to manufacturing processes in response to unexpected changes, a feature called context awareness. Recent advances in interpreting context data in the semantic web have permitted SMS to understand the active situation of manufacturing processes. This paper presents a literature analysis of context-aware workflow management approaches in the smart manufacturing domain, with a particular focus on semantic web-based approaches published from 2015 to 2022. A Systematic Literature Review (SLR) methodology was applied to analyze the state-of-the-art via the PICOC method. The contributions of this work are (1) an SLR about context-aware workflow management for smart manufacturing systems focusing on semantic web-based approaches, (2) a systematic taxonomy to break down the approaches in conformity based on content and main workflow management function area, and (3) identification of opportunities for improvement in technical features such as context awareness, use case implementation, tools employed, licensing, security, and scalability. A novel architecture and components are also proposed to address the identified active challenges. • Systematic review on semantic web & context-aware workflows for smart manufacturing. • Technical taxonomy for semantic web-enhanced workflow management solutions. • Tools, algorithms, and techniques for self-reconfigurable workflows. • Architecture for Context-Aware Workflow Management: An AAS-based approach.

This paper presents an industrial scenario that simulates a Manufacturing as a Service system for the execution of remote production orders built upon the implementation of emerging Asset Administration Shell (AAS) capabilities and International Data Space connectors. Static and dynamic information from industrial assets (presses and laser cutting machines) are modelled with new AAS submodels and the result is stored in an AAS manager/registration system. A manufacturing orchestrator discovers assets through the registry and completes production orders. The AAS registry allows the selection of assets with capabilities to perform tasks and also shares the AAS catalogue available in the system. The catalogue is shared with external parties through Data Space Connectors. Third party companies can launch manufacturing orders remotely using the same connectors. The paper validates the implementation of AAS components and IDS connectors in a manufacturing context where remote production orders can be securely activated.

Microservice Architectures have increasingly become popular in Industry 4.0 as they allow heterogeneous systems to interact, reduce the complexity in the management of individual components, and support distributed deployments. The integration of those distributed services into orchestrated production processes is performed by workflow managers. Next generation workflow managers must overcome a number of challenges when operating in microservice architectures and IoT environments. To overcome these challenges (heterogeneity, high dynamism, edge deployment or scalability), we propose a work-flow manager alternative built in Node-RED. Node-RED provides instruments for the development of IoT systems and leverages the edge computing paradigm. This solution is deployable in embedded systems, is able to load and execute business processes by means of BPMN recipes and enables the integration of other frameworks and architectures.

Cities in the 21st century play a major role in the sustainability and climate impact reduction challenges set by the European agenda. As the population of cities grows and their environmental impact becomes more evident, the European strategy aims to reduce greenhouse gas emissions-the main cause of climate change. Measures to reduce the impact of climate change include reducing energy consumption, improving mobility, harnessing resources and renewable energies, integrating nature-based solutions and efficiently managing infrastructure. The monitoring and control of all this activity is essential for its proper functioning. In this context, Information and Communication Technology (ICT) plays a key role in the digitisation, monitoring, and managing of these different verticals. Urban data platforms support cities on extracting Key Performance Indicators (KPI) in their efforts to make better decisions. Cities must be transformed by applying efficient urban planning measures and taking into account not only technological aspects, but also by applying a holistic vision in building solutions where citizens are at the centre. In addition, standardisation of platforms where applications are integrated as one is necessary. This requires interoperability between different verticals. This article presents the information platform developed for the city of Vitoria-Gasteiz in Spain. The platform is based on the UNE 178104 standard to provide a holistic architecture that integrates information from the different urban planning measures implemented in the city. The platform was constructed in the context of the SmartEnCity project following the urban transformation strategy established by the city. The article presents the value-added solutions implemented in the platform. These solutions have been developed by applying co-creation techniques in which stakeholders have been involved throughout the process. The platform proposes a step forward towards standardization, harmonises the integration of data from multiple vertical, provides interoperability between services, and simplifies scalability and replicability due to its microservice architecture.

In recent years, the usage of mobile browsers has experienced an astonishing growth. Nowadays, most citizens use their mobile phones instead of their laptops to surf the Web for immediate availability. Nevertheless, Web design is performed considering laptops screen dimensions and websites are readjusted to mobile screen resolutions using Responsive Web Design. This conversion to smaller screen resolutions causes some drawbacks to mobile navigation. Web Augmentation is an effective methodology that allows end-users to customize third party websites according to their needs. This technique can mitigate the problems caused by small screen resolutions.This article introduces MAWA, a Firefox mobile browser extension for Web Augmentation designed to remove and move content in any website using a visual programming technique. The article presents the benefits introduced by MAWA in response to the drawbacks arising when websites are adapted from laptop screen resolutions to mobile dimensions. Evaluation is also introduced in this article where end-users with no programming knowledge have adapted four different websites using the extension. Results show that testers claim that drawbacks presented by mobile browsers are mitigated with the utilization of MAWA.

The use of the World Wide Web has experienced extraordinary growth in the last decades. The Web has become the main source of information for millions of users. The number of websites offering content to users is countless. In order to personalise information according to their needs, users often have to visit multiple, unconnected pages. Users perform a number of actions to collect that information that requires concentration. If the number of Web resources is large, the activity becomes unpleasant. The problem increases when these tasks are performed frequently and repetitively. These tasks are time-consuming and lead users to experience frustration and disorientation during the activity, causing a loss of concentration that prolongs the activity over time. Web Augmentation combines different Web technologies to improve user experience on existing pages by adding content from different pages among other benefits. This article proposes Web Augmentation as a technique to reduce user interactions in repetitive tasks. To support the proposal, the paper introduces Excore, a browser extension for Web Augmentation that allows end-users to add content from different resources automatically. The article presents the benefits introduced by this approach as a response to the drawbacks experienced by users while performing their activities on the Web. The architecture of the platform and its operations are described by means of an example. A double evaluation of the extension is addressed, one qualitative and one quantitative. The results show that Excore reduces the number of interactions by 94.45% and the time to complete a task by 80.75%.

The new paradigm of the Industry 4.0 centers on the digitalization of assets to realize a new industrial revolution. Standardization and interoperability are key for the successful implementation of this digitalization strategy. Among the different standardization and interoperability initiatives, Asset Administration Shell (AAS) proposes a standardized electronic representation of industrial assets enabling Digital Twins and interoperability between automated industrial systems and Cyber Physical System (CPS). In this context, Mondragon Corporation has launched several initiatives to boost the digitalization of its industries. Although implementation of the AAS in real industrial scenarios is not widespread, Mondragon Corporation has identified this initiative as a key enabler for manufacturing companies within its group. This paper presents a case study on the application of the AAS in an industrial context. The AAS initiative is implemented through integrating a Machine Tooling ecosystem with a robotic arm. This implementation facilitates the discovery and integration of grinding machines with other components or machines in a production plant, validating the AAS in a manufacturing scenario.

The growth of the web has been unstoppable in the last decade, which leads to an increasing demand for extracting information from it. Apart from the need to extract information, this growth also has brought the necessity to adapt web pages to user requirements, create annotations or test web applications. Due to the evolution of web pages, the complexity of the implementation of these techniques has increased. Being able to test, annotate, adapt and extract information from web pages correctly and efficiently has become a primary task. In order to perform all these tasks, it is mandatory to have the best mechanisms to effectively and unequivocally locate the desired elements throughout the web page life cycle, especially when a web page evolves. Different mechanisms are used to find web nodes. These mechanisms, called locators, are prone to fail over time owing to changes on websites. Many authors improve life expectancy of locators developing algorithms that use different types of locators. Some others have created algorithms that regenerate locators by saving extra information from the previous structure of the website. These algorithms extend the useful life of locators but their computational and storage cost is much higher. To avoid these problems, we have designed an algorithm that employs an attribute system embedded in the HTML code. The algorithm is able to regenerate the locators based on these attributes every time a single change takes place in a given element attribute. The evaluation of the proposal shows a much lower computational cost than in previous works.

The WorldWide Web has endured an incredible growth in the last decades. Nowadays, we can visit an unimaginable number of websites from different devices (laptops, mobiles, tablets...) in order to obtain immediate information. However, this information is separated in different resources and Web information search is unpleasant. The feeling of the users is frustrating when collecting information from different resources. Techniques such as Web personalization and Web customization have been an important research area during the last years. Web customization techniques have been widely used to develop website adaptation along the WorldWide Web. This customization is frequently performed by end-users that use the numerous tools available to carry out this assignment. This article presents Excore, a Web customization tool that permits end-users to customize their websites with automatic Web searches. The article presents the benefits introduced by Excore as a response to the drawbacks end-users experience while they perform their Web activities. Evaluation of the Web customization tool is also addressed in the paper. Evaluation is performed with stakeholders by means of tests and surveys. Results show that testers overcome the detected drawbacks with the use of Excore.

Technology Watch human agents have to read many documents in order to manually categorize and dispatch them to the correct expert, that will later add valued information to each document. In this two step process, the first one, the categorization of documents, is time consuming and relies on the knowledge of a human categorizer agent. It does not add direct valued information to the process that will be provided in the second step, when the document is revised by the correct expert. This paper proposes Machine Learning tools and techniques to learn from the manually pre-categorized data to automatically classify new content. For this work a real industrial context was considered. Text from original documents, text from added value information and Semantic Annotations of those texts were used to generate different models, considering manually preestablished categories. Moreover, three algorithms from different approaches were used to generate the models. Finally, the results obtained were compared to select the best model in terms of accuracy and also on the reduction of the amount of document readings (human workload).