INESCC-DL - Artigos em Livros de Actas
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- Predicting Order Activity Sequence Using Contextual Process MiningPublication . Barbeiro, Diogo A.; Martinho, Ricardo F. G.; Ferreira, Carlos J. R.Logistics processes depend heavily on changing conditions and making accurate forecasts is becoming more and more important to preventing delays and/or predict risks. Predictive Process Monitoring has advanced through deep learning and process-mining approaches, yet current methods often lack interpretability and lose accuracy when context varies. Recent research shows that contextual factors can improve predictions, but their integration into transparent, model-driven frameworks remains limited. This article presents a context-aware predictive approach that filters historical event logs by important attributes, discovers process models with the Inductive Miner, and predicts future activities and timestamps using token-based replay and polynomial regression. Experiments with real logistics data show that incorporating context reduces prediction errors, while the use of process mining ensures an interpretable and operationally practical forecasting solution for logistics environments.
- VE4OCPM: An Object-Centric Process Mining Variant Explorer Visualisation ApproachPublication . Gaspar, Marco A. P.; Martinho, Ricardo F. G.; Ferreira, Carlos J. R.This work presents the Variant Explorer, a dual-mode visualisation for Object-Centric Process Mining that reveals control-flow variability and object interactions across process variants, combining object details and subway-map views for intuitive analysis of complex processes. Understanding process variants is essential to detect deviations, exceptions, and improvement opportunities. However, traditional process mining tools assume one case per process instance, which do not work well when events involve multiple objects, such as orders, products, and packages. Existing Object-Centric approaches, like OCπ and Object-Centric Process Analysis (OCPA), introduced models and libraries to support multi-object analysis, but many face technical and usability limitations. To address this gap, this proposal provides two complementary visualisations that show object participation and variant differences side-by-side. Evaluation with real logistics data and user interpretation tests shows that users are able to identify repetitions, skipped activities, deviations, and object interactions clearly. This work offers a practical and interpretable solution for variant analysis in Object-Centric Process Mining (OCPM) and supports better understanding for analysts and business stakeholders.
- Mental health indicators in the hospitalization process in a Brazilian psychosocial care networkPublication . Lima, Inacia Bezerra de; Alves, Domingos; Vinci, Andre Luiz Teixeira; Rijo, Rui Pedro Charters Lopes; Martinho, Ricardo; Yamada, Diego Bettiol; Bernardi, Filipe Andrade; Furegato, Antonia Regina FerreiraWe aim to present the use and viability of mental health indicators at a Brazilian reference psychiatric hospital. We elaborated a Business Process Model and Notation based model of the patients' hospitalization process based on semi-structured interviews with managers and professionals of the hospital. We analyzed the model and selected a set of 6 mental health indicators, based on evidence-based practice from other countries, using information from several Health Information Systems regarding hospitalizations from 2013 to 2017. In Brazil, there is a lack of methods for the manager to measure the actions carried out in mental health. Thus, the method proposed in this article can be used as metrics to assess the impact of public policy implementation and to assist planning and decision-making based on evidence in mental health.
- TBI Score - use of a mobile score system to aid the diagnosis of tuberculosis in children in BrazilPublication . Bernardi, Filipe Andrade; Lima, Vinicius Costa; Sampaio, Danilo Maglio; Santos, Marcelo Cordeiro dos; Rijo, Rui Pedro Charters Lopes; Alves, DomingosTuberculosis (TB) is a bacterial infectious disease that mainly affects the lungs and remains as one of the biggest public health problems in the world. The treatment methods currently available can cure almost all cases. Due to the difficulty of bacteriological confirmation of TB in children, the Brazilian Ministry of Health recommended the use of a scoring system for the diagnosis of pulmonary TB in childhood, covering aspects of clinical, radiological and epidemiological data. The general objective of this work is the development and availability of a mobile application based on the score described in the Manual of Recommendations for TB Control in Brazil. The application was organized to make the questionnaire flow linear, while maintaining the accordance with the structure presented in the manual. The score adapted to the Brazilian context allows health professionals to underpin their decisions with reliable information.
- End-to-End Management System Framework for Smart Public BuildingsPublication . Jesus, Ivo; Pereira, Tomás; Marques, Pedro; Sousa, João; Perdigoto, Luís; Coelho, PauloThis paper presents a project aiming to design a complete framework to measure energy (electricity and gas) and water consumptions in a local Parish Council building and an adjacent Sports Hall located in the central part of Portugal. The goal is an end-to-end solution, from data acquisition to data analysis. Besides acquiring and storing the data, the aim is to make this information available and valuable to enhance better decisions in building management actions, to enable detection of situations of anomalous consumption and also to promote building users' awareness. To pursue this goal, PLCnext technology solutions from Phoenix Contact are adopted. The system is based on a new generation industrial controller that communicates with energy and water meters distributed throughout the building using a standard Information Technology (IT) network. The solution explores Industry 4.0 concept, such as Cloud Data Management, Cybersecurity, and Machine Learning. With historic consumption records available, Machine Learning strategies are being used to predict load profiles in a short-term horizon and also planned to classify untypical consumption behaviors (for monitor and alarm purposes). This project is being deployed in partnership between Polytechnic of Leiria, EduNet International Education Network and involving the local Parish Council, owner of the monitored buildings.
- Machine Learning Methods for Quality Prediction in Thermoplastics Injection MoldingPublication . Silva, Bruno; Sousa, João; Alenya, GuillemNowadays, competitiveness is a reality in all industrial fields and the plastic injection industry is not an exception. Due to the complex intrinsic changes that the parameters undergo during the injection process, it is essential to monitor the parameters that influence the quality of the final part to guarantee a superior quality of service provided to customers. Quality requirements impose the development of intelligent systems capable to detect defects in the produced parts. This article presents a first step towards building an intelligent system for classifying the quality of produced parts. The basic approach of this work is machine learning methods (Artificial Neural Networks and Support Vector Machines) and techniques that combine the two previous approaches (ensemble method). These are trained as classifiers to detect conformity or even defect types in parts. The data analyzed were collected at a plastic injection company in Portugal. The results show that these techniques are capable of incorporating the non-linear relationships between the process variables, which allows for a good accuracy (≈99%) in the identification of defects. Although these techniques present good accuracy, we show that taking into account the history of the last cycles and the use of combined techniques improves even further the performance. The approach presented in this article has a number of potential advantages for online predicting of parts quality in injection molding processes.
- An Approach to Assess the Performance of Mobile Applications: A Case Study of Multiplatform Development FrameworksPublication . Mota, Dany; Martinho, RicardoComparative studies between software multiplatform development frameworks lack a proper approach that can be replicated in future performance assessments. Moreover, there is still a deficit in performance comparison tools. Also, performance comparisons realized between mobile applications developed under these multiplatform frameworks should be done with applications running in Release Mode, which ends up not happening in most studies. The objective of this paper is thus to create a whole comparative process as correct and stable as possible, so that we can use it to safely assess performance of mobile applications developed with these frameworks. As a case study, we compare the well-known Flutter and React Native frameworks, and present the obtained results under the proposed approach. With this work, developers can not only assess both these particular frameworks, but also use the approach for further comparisons.
- Architectural Challenges on the Integration of e-Commerce and ERP Systems: A Case StudyPublication . Santos, Fábio; Martinho, RicardoMany retail companies had to go online before their Enterprise Resource Planning (ERP)-type systems were ready to fulfill all business requirements. Their overall daily operation still heavily depends on these highly customized systems often mandatory because of legal obligations, which frequently come without e-commerce “off the shelf” integration. This paper identifies main challenges derived out of the architectural and integration requirements from a case study at an e-tailer company that operates via two sales channels: online store and third-party marketplaces. These challenges led to the definition of a system architecture and implementation considerations for this common integration scenario, which was validated through its implementation. Our proposed approach allows ERP-dependent organizations to start selling online with open-source technologies, avoiding extra ERP licensing and hidden maintenance costs.
- Process Mining for IS Project Success Factors Management: A proposalPublication . Pedrosa, Joana; Varajão, João; Magalhaes, Luis Gonzaga; Martinho, RicardoResearch on Success Factors (SF) of Information Systems (IS) projects carried out over the last decades has resulted in a vast literature. However, extant studies typically aim to identify and list generic SF for projects, denoting a static perspective, with few concerns of practical nature regarding their use as management tools to support decisions throughout the projects’ lifecycle. On the other hand, process mining has been used to discover, analyze, and improve project management processes. In this paper, we propose a new approach that involves relating the performance of those processes with SF in IS projects. By using process mining, the aim is to automatically extract and manage SF in projects, measure processes performance, and provide project managers with information on how SF correlate with performance. This will provide managers with enhanced information regarding status and improvement opportunities for current and future projects. The main purpose is to contribute to the project management theory and practice by providing a decision support system that can associate performance with IS projects’ SF automatically obtained from internal and external data sources.
- Sustainability study of limestone quarry waste into value-added products: PCC and GCCPublication . Monteiro, S. M. C. S.; Jorge, Muanassa; Heleno, M. L.; Oliveira, N. S.; Alves, M. L.; Veiga, A.; Silva, A.Calcium carbonate can be obtained naturally from limestone, chalk, marble, and other sedimentary rock forms. Ground calcium carbonate (GCC) and precipitated calcium carbonate (PCC) are two materials that can be produced from natural calcium carbonate. The future of GCC and PCC is promising since they are linked to industries with high demand, such as packaging, building & construction, transportation, and industrial applications, with an expected compound annual growth rate higher than 4% until 2027. This research focuses on the production of GCC and PCC through a comparative analysis that identifies the macro conditions that become advantageous to produce and commercialising PCC in a quarry context. This allows a valorisation of the limestone waste resulting from the extraction operations, converting it into by-products of the process. Not all existing limestone quarries in the Serra de Aire e Candeeiros region are suitable sources of raw material to produce PCC. The five extraction poles were identified with the potential for extracting suitable raw materials, associated with several companies dedicated to the extraction operation in these quarries.