INESCC-DL - Artigos em Livros de Actas
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Percorrer INESCC-DL - Artigos em Livros de Actas por Domínios Científicos e Tecnológicos (FOS) "Ciências Naturais::Ciências da Computação e da Informação"
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- 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.
- Blind Guide: An Ultrasound Sensor-based Body Area Network for Guiding Blind PeoplePublication . Pereira, António; Nunes, Nelson; Vieira, Daniel; Costa, Nuno; Fernandes, Hugo; Barroso, JoãoWireless Sensor Networks, in particular Wireless Body Area Networks, is a technology suggested by the research community as allowing elderly people, or people with some kind of disability, to live in a safer, responsive and comfortable environment while at their homes. One of the most active threats to the autonomous life of blind people is the quantity and variety of obstacles they face while moving, whether they are obstacles in the footpath or obstacles coming out from the walls of buildings. Hence, it is necessary to develop a solution that helps or assists blind people while moving either in indoor or outdoor scenarios, simultaneously allowing the use of the use of white cane or the Seeing Eye dog. In this article, the authors propose the use of an ultra-sound based body area network for obstacle detection and warning as a complementary and effective solution for aiding blind people when moving from place to place. According to the cost estimates of the solution and to the negligible setup time, this could be a real effective complementary solution for blind people.
- Data Acquisition and Monitoring System for Legacy Injection MachinesPublication . Silva, Bruno; Sousa, João; Alenya, GuillemNowadays, companies must embrace the concept of Digitalization and Industry 4.0 to remain competitive in the market. The reality is that most of them do not have their industrial devices prepared to access their data on a real-time basis. As most companies do not have the possibility to renew all their legacy devices and because these devices are still very productive, a retrofit solution is of high interest. In this work, we propose an affordable procedure that allows data collection and monitoring of older injection machines, as a contribution towards legacy devices integration. The developed system neither requires additional proprietary modules, nor contractual annual fees for different devices, sharing the same interface across different machine manufacturers and also contributing to uniform data collection. Evaluation was carried out in a real shop floor, monitoring the injection parameters for different machine models, validating the effectiveness of the developed system.
- An Evolutionary Algorithm based on an outranking relation for sorting problemsPublication . Oliveira, Eunice; Antunes, Carlos HenggelerA new approach for using the preferences elicited from a Decision Maker (DM) into the operational framework of an Evolutionary Algorithm (EA) is presented. The preference representation is achieved using the parameters and principles of the ELECTRE TRI method devoted to the sorting problem. The outranking relation is used to replace the non-dominance relation in the usual operators in the EA (crossover, mutation and selection operator). The aim of this approach is to focus the search on the region of interest defined by the DM's preferences and consequently restrict the number of solutions in the Pareto-optimal front to be subject to further screening. This aspect is particularly important when dealing with problems that lead to a large number of non-dominated solutions.
- 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.
- 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.
- On the information provided by uncertainty measures in the classification of remote sensing imagesPublication . Gonçalves, Luisa; Fonte, Cidália C.; Júlio, Eduardo N.B.S.; Caetano, MarioThis paper investigates the potential information provided to the user by the uncertainty measures applied to the possibility distributions associated with the spatial units of an IKONOS satellite image, generated by two fuzzy classifiers, based, respectively, on the Nearest Neighbour Classifier and the Minimum Distance to Means Classifier. The deviation of the geographic unit characteristics from the prototype of the class to which the geographic unit is assigned is evaluated with the Un non-specificity uncertainty measures proposed by [1] and the exaggeration uncertainty measure proposed by [2]. The classifications were evaluated using accuracy and uncertainty indexes to determine their compatibility. Both classifications generated medium to high levels of uncertainty for almost all classes, and the global accuracy indexes computed were 70% for the Nearest Neighbour Classifier and 53% for the Minimum Distance to Means Classifier. The results show that similar conclusions can be obtained with accuracy and uncertainty indexes and the latter, along with the analysis of the possibility distributions, may be used as indicators of the classification performance and may therefore be very useful tools. Since the uncertainty indexes may be computed to all spatial units, the spatial distribution of the uncertainty was also analysed. It's visualization shows that regions where less reliability is expected present a great amount of detail that may be potentially useful to the user.
- 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.
- 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.