INESCC-DL - Artigos em Revistas Internacionais
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- A simple heuristic for the identification of the case ID attribute in unlabelled process mining event logsPublication . Vicente, André; Grilo, Carlos; Rijo, Rui; Martinho, RicardoThis study addresses the critical challenge of identifying and labelling the case ID attribute in unlabelled event logs, a fundamental task in process mining. Case IDs uniquely associate events with individual process instances, enabling accurate analysis and discovery of operational insights. Manual identification of case IDs is error-prone and labour-intensive, often hindering the scalability and reliability of process mining analyses. This paper introduces a novel heuristic method that automates case ID identification, improving efficiency and accuracy for diverse real-world datasets. The proposed heuristic leverages unique temporal patterns observed in event logs to distinguish case ID attributes from other attributes. It calculates a weighted average of temporal spans and applies customisable parameters to prioritise relevant attributes. The method was validated using 27 datasets from the Business Process Intelligence (BPI) Challenge, representing a variety of industries and event log complexities. Performance metrics, including success rates and computational efficiency, were benchmarked against existing approaches. The heuristic achieved an 85.2% top-1 success rate, and remains effective provided at least one repeating categorical attribute is present - a condition met by virtually all publicly available business and industrial logs. It consistently ranked case IDs among the top attributes even in challenging scenarios, such as cyclic processes and multi-correlated data. The method demonstrated robustness across diverse datasets, processing large event logs within seconds, highlighting its practicality for real-world applications. This research contributes an innovative and explainable approach to case ID identification that requires only raw event logs, contrasting with existing methods reliant on pre-labelled data or complex pipelines. Its simplicity, efficiency, and adaptability to various process types make it a valuable tool for advancing process mining capabilities.
- Managing Data in Screening Programs: Challenges and SolutionsPublication . Monteiro, Hugo; Oliveira, Mariana; Martinho, Ricardo; Martins, CarlosPopulation-based screening programs are vital public health initiatives that enable the early detection of diseases, significantly reducing both morbidity and healthcare costs. As these programs expand, the management of the extensive data they generate becomes increasingly complex, highlighting the need for structured digital solutions. This narrative review article presents a pragmatic framework aimed at clarifying big data analytics tailored to the needs and practices of healthcare professionals and administrators, focusing on effective integration into routine screening workflows. To achieve effective data utilization, the process begins with systematic archiving, which involves cloud-based storage solutions capable of securely maintaining various data formats in compliance with regulatory standards, thus ensuring long-term accessibility and continuity. Subsequent real-time processing of screening data facilitates rapid decision-making and patient management by providing immediate validation and analysis, essential for maintaining the responsiveness of screening services. Transformation processes play a critical role in converting diverse data inputs into standardized, consistent formats, enabling seamless communication and exchange among multiple healthcare systems. Integration further builds upon this standardization, merging data from different healthcare providers and diagnostic centers into centralized analytical platforms. This unified approach enables comprehensive patient monitoring and supports predictive modeling for early identification of at-risk individuals. Advanced analytics, particularly process mining and predictive techniques, reveal inefficiencies within screening workflows, highlighting areas needing improvement. These methods help healthcare managers to streamline operations, optimize resources, and enhance overall program performance. Real-time visualization tools provide administrators with continuous, practical insights into operational dynamics, despite existing challenges related to data governance and system interoperability. This article illustrates these concepts through concrete examples from the colorectal cancer screening program in Northern Portugal and the response to the COVID-19 pandemic. The colorectal cancer screening scenario demonstrates how structured data management significantly boosts operational efficiency and healthcare accessibility. Meanwhile, the COVID-19 experience highlights the importance of having flexible digital infrastructures capable of quickly adapting to unexpected crises. Finally, ongoing investments in digital infrastructure, professional training, and comprehensive data governance are crucial for sustaining these improvements. This review provides clear, actionable knowledge to support healthcare professionals in adopting big data analytics effectively within preventive healthcare programs.
- A Data-Driven Approach to Forecasting Heating and Cooling Energy Demand in an Office Building as an Alternative to Multi-Zone Dynamic SimulationPublication . Godinho, Xavier; Bernardo, Hermano; Sousa, João C. de; Oliveira, Filipe T.Nowadays, as more data is now available from an increasing number of installed sensors, load forecasting applied to buildings is being increasingly explored. The amount and quality of resulting information can provide inputs for smarter decisions when managing and operating office buildings. In this article, the authors use two data-driven methods (artificial neural networks and support vector machines) to predict the heating and cooling energy demand in an office building located in Lisbon, Portugal. In the present case-study, these methods prove to be an accurate and appealing alternative to the use of accurate but time-consuming multi-zone dynamic simulation tools, which strongly depend on several parameters to be inserted and user expertise to calibrate the model. Artificial neural networks and support vector machines were developed and parametrized using historical data and different sets of exogenous variables to encounter the best performance combinations for both the heating and cooling periods of a year. In the case of support vector regression, a variation introduced simulated annealing to guide the search for different combinations of hyperparameters. After a feature selection stage for each individual method, the results for the different methods were compared, based on error metrics and distributions. The outputs of the study include the most suitable methodology for each season, and also the features (historical load records, but also exogenous features such as outdoor temperature, relative humidity or occupancy profile) that led to the most accurate models. Results clearly show there is a potential for faster, yet accurate machine-learning based forecasting methods to replace well-established, very accurate but time-consuming multi-zone dynamic simulation tools to forecast building energy consumption.
- Heating and Cooling Degree-Days Climate Change Projections for PortugalPublication . Andrade, Cristina; Mourato, Sandra; Ramos, JoãoClimate change is expected to influence cooling and heating energy demand of residential buildings and affect overall thermal comfort. Towards this end, the heating (HDD) and cooling (CDD) degree-days along with HDD + CDD were computed from an ensemble of seven high-resolution bias-corrected simulations attained from EURO-CORDEX under two Representative Concentration Pathways (RCP4.5 and RCP8.5). These three indicators were analyzed for 1971-2000 (from E-OBS) and 2011-2040, and 2041-2070, under both RCPs. Results predict a decrease in HDDs most significant under RCP8.5. Conversely, it is projected an increase of CDD values for both scenarios. The decrease in HDDs is projected to be higher than the increase in CDDs hinting to an increase in the energy demand to cool internal environments in Portugal. Statistically significant linear CDD trends were only found for 2041-2070 under RCP4.5. Towards 2070, higher(lower) CDD (HDD and HDD + CDD) anomaly amplitudes are depicted, mainly under RCP8.5. Within the five NUTS II regions projections revealed for 2041-2070 a decrease in heating requirements for Algarve and Lisbon Area higher in Faro, Lisboa and Setúbal whereas for North and Center regions results predicts an increase in cooling energy demand mainly in Bragança, Vila Real, Braga, Viana do Castelo, Porto and Guarda, higher under RCP8.5.
- A finite element model of an induction motor considering rotor skew and harmonicsPublication . Oliveira, F. T.; Donsión, M. P.Finite element analysis is widely used in engineering, and has for some time been used in modelling the behaviour of an induction motor. Limitations and challenges of this approach will be addressed over a case-study commercial 0,37 kW, 4-pole squirrel-cage induction motor simulated using two-dimensional software FEMM. A few notes on the consideration of rotor skew and harmonic distortion in such a model are also included.
- Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial imagesPublication . Andriolo, Umberto; Gonçalves, Gil; Rangel-Buitrago, Nelson; Paterni, Marco; Bessa, Filipa; Gonçalves, Luisa M. S.; Sobral, Paula; Bini, Monica; Duarte, Diogo; Fontán-Bouzas, Ángela; Gonçalves, Diogo; Kataoka, Tomoya; Luppichini, Marco; Pinto, Luis; Topouzelis,Konstantinos; Vélez-Mendoza, Anubis; Merlino, SilviaUnmanned aerial systems (UAS, aka drones) are being used to map macro-litter on the environment. Sixteen qualified researchers (operators), with different expertise and nationalities, were invited to identify, mark and categorize the litter items (manual image screening, MS) on three UAS images collected at two beaches. The coefficient of concordance (W) among operators varied between 0.5 and 0.7, depending on the litter parameter (type, material and colour) considered. Highest agreement was obtained for the type of items marked on the highest resolution image, among experts in litter surveys (W = 0.86), and within territorial subgroups (W = 0.85). Therefore, for a detailed categorization of litter on the environment, the MS should be performed by experienced and local operators, familiar with the most common type of litter present in the target area. This work provides insights for future operational improvements and optimizations of UAS-based images analysis to survey environmental pollution.
- Heatsinks to Cool Batteries for Unmanned Aerial VehiclesPublication . Galvão, J.; Faria, P.; Mateus, A.; Pereira, T.; Fernandes, S.This study aims to develop several different models of heatsinks, designed to cool a vertical take-off and landing unmanned aerial vehicle (UAV) battery, through topology optimization, aimed at being manufactured through selective laser melting (SLM) technology. A battery’s temperature must be properly managed for a safe and efficient operation. The methodology developed was with the support of software to carry out several simulations which, starting from several scenarios and restrictions imposed by the small space available to accommodate these small batteries in this type of aircraft. The conception resulted in several battery thermal management systems (BTMS) models, with different applications and efficiency degrees. A relevant aspect is the topology optimization being coupled to computational thermal analysis to reduce the mass of the heatsink whilst ensuring a maximum battery temperature threshold. Together with the use of topology optimization, the SLM process was selected to manufacture the heat sinks, under conditions of geometric freedom, using several high thermal conductivity metal alloys, such as, aluminium and copper to obtain the designed models.
- A distributed multiagent system architecture for body area networks applied to healthcare monitoringPublication . Felisberto, Filipe; Laza, Rosalía; Fdez-Riverola, Florentino; Pereira, AntónioIn the last years the area of health monitoring has grown significantly, attracting the attention of both academia and commercial sectors. At the same time, the availability of new biomedical sensors and suitable network protocols has led to the appearance of a new generation of wireless sensor networks, the so-called wireless body area networks. Nowadays, these networks are routinely used for continuous monitoring of vital parameters, movement, and the surrounding environment of people, but the large volume of data generated in different locations represents a major obstacle for the appropriate design, development, and deployment of more elaborated intelligent systems. In this context, we present an open and distributed architecture based on a multiagent system for recognizing human movements, identifying human postures, and detecting harmful activities. The proposed system evolved from a single node for fall detection to a multisensor hardware solution capable of identifying unhampered falls and analyzing the users’ movement. The experiments carried out contemplate two different scenarios and demonstrate the accuracy of our proposal as a real distributed movement monitoring and accident detection system. Moreover, we also characterize its performance, enabling future analyses and comparisons with similar approaches.
- Removing Barriers to Promote Social Computing among Senior PopulationPublication . Marcelino, Isabel; Laza, Rosalía; Fdez-Riverola, Florentino; Pereira, AntónioSmartphones and tablets proliferation enabled by accessible prices and also by the inclusion of sensing abilities promotes their use in several areas, such as healthcare. It opens new horizons in the field of continuous and noninvasive monitoring and support to population, namely, to seniors. Despite the great benefits that mobile sensing and social computing could provide to increase elderly’s quality of life, many studies have shown that elderlies deal with difficulty with Information and Communication Technology (ICT). In this paper we present a solution to overcome barriers between elderlies and their ICT usage in order to potentiate all the benefits provided from mobile sensing and social computing. A survey on guidelines, standards, and advice regarding usability and accessibility issues when developing solutions for elderly people was carried out. This survey was made having in mind that senior population have singular requirements due to age related changes and also frequently technological illiteracy. We have identified and applied the most important guidelines to our solution. A prototype was made using responsive design in order to be adaptable to any type of devices. Regarding evaluation, usability tests and semistructured interviews were conducted in real scenario.
- The small world of efficient solutions: empirical evidence from the bi-objective {0,1}-knapsack problemPublication . Silva, Carlos Gomes da; Clímaco, João; Filho, Adiel AlmeidaThe small world phenomenon, Milgram (1967) has inspired the study of real networks such as cellular networks, telephone call networks, citation networks, power and neural networks, etc. The present work is about the study of the graphs produced by efficient solutions of the bi-objective {0,1}-knapsack problem. The experiments show that these graphs exhibit properties of small world networks. The importance of the supported and non-supported solutions in the entire efficient graph is investigated. The present research could be useful for developing more effective search strategies in both exact and approximate solution methods of {0,1} multi-objective combinatorial optimization problems.