CISeD - CENTRO DE ESTUDOS EM SERVIÇOS DIGITAIS
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Percorrer CISeD - CENTRO DE ESTUDOS EM SERVIÇOS DIGITAIS por Domínios Científicos e Tecnológicos (FOS) "Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática"
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- Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case StudyPublication . Abbasi, Maryam; Bernardo, Marco V.; Vaz, Paulo; Silva, José; Martins, Pedro; ANTUNES VAZ, PAULO JOAQUIM; Silva, JoséThe increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, hetero geneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.
- An integrated and interoperable AutomationML-based platform for the robotic process of metal additive manufacturingPublication . Babcinschi, Mihail; Freire, Bernardo; Ferreira, Lucía; Señaris, Baltasar; Vidal, Felix; Vaz, Paulo; Neto, PedroIncreasingly, industry is looking to better integrate their industrial processes and related data. Interoperability is key since the organizations need to share data between them, between departments and the different stages of a given technological process. The problem is that many times there are no standard data formats for data exchange between heterogeneous engineering tools. In this paper we present an integrated and interoperable AutomationML-based platform for the robotic process of metal additive manufacturing (MAM). Data such as the MAM robot targets and process parameters are shared and edited along the different sub-stages of the process, from Computer-Aided Design (CAD), to path planning, to multiphysics simulation, to robot simulation and production. The AutomationML neutral data format allows the implementation of optimization loops connecting different sub-stages, for example the multi-physics simulation and the path planning. A practical use case using the Direct Energy Deposition (DED) process is presented and discussed. Results demonstrated the effectiveness of the proposed AutomationML-based solution.
- Data Privacy and Ethical Considerations in Database ManagementPublication . Pina, Eduardo; Ramos, José; Jorge, Henrique; ANTUNES VAZ, PAULO JOAQUIM; Vaz, Paulo; Silva, José; Wanzeller, Cristina; Abbasi, Maryam; Martins, Pedro; Silva, José; Wanzeller Guedes de Lacerda, Ana CristinaData privacy and ethical considerations ensure the security of databases by respecting individual rights while upholding ethical considerations when collecting, managing, and using information. Nowadays, despite having regulations that help to protect citizens and organizations, we have been presented with thousands of instances of data breaches, unauthorized access, and misuse of data related to such individuals and organizations. In this paper, we propose ethical considerations and best practices associated with critical data and the role of the database administrator who helps protect data. First, we suggest best practices for database administrators regarding data minimization, anonymization, pseudonymization and encryption, access controls, data retention guidelines, and stakeholder communication. Then, we present a case study that illustrates the application of these ethical implementations and best practices in a real-world scenario, showing the approach in action and the benefits of privacy. Finally, the study highlights the importance of a comprehensive approach to deal with data protection challenges and provides valuable insights for future research and developments in this field
- Designing Inclusive Smartwatch Interfaces: Guidelines for Enhancing Usability and Adoption Among Older AdultsPublication . P. Duarte , Rui; Alves, Valter; Alves, Valter; Mota, MikaelAging introduces sensory, motor, and cognitive challenges and limited familiarity with digital interfaces, often hindering older adults’ adoption of new technologies. Smartwatches, with their compact size and health monitoring features, promise to improve older adults’ quality of life. However, their small screens and complex interfaces create significant usability barriers. While guidelines for mobile and web interfaces exist, frameworks for smartwatch design still need to be explored. This study addresses this gap by proposing smartwatch-specific design guidelines for older adults. Through an analysis of user challenges, existing design principles, and smartwatch constraints, the research formulates actionable recommendations to enhance usability and user experience. The contributions include identifying key obstacles older adults face with smartwatches, evaluating the applicability of established guidelines, creating tailored design principles for small screens, and developing a design system that balances simplicity, usability, and functionality. These contributions aim to facilitate smartwatch adoption and improve the inclusivity of digital technologies for older adults.
- Performance Comparison of Redis, Memcached, MySQL, and PostgreSQL: A Study on Key-Value and Relational DatabasesPublication . Almeida, Dany; Lopes, Maria; Saraiva, Luzia; Abbasi, Maryam; Martins, Pedro; Silva, José; ANTUNES VAZ, PAULO JOAQUIMThis paper investigates and compares the performance of relational databases (MySQL and PostgreSQL) and key-value databases (Memcached and Redis) under various test loads. The study utilizes the Yahoo! Cloud Serving Benchmark to simulate diverse workloads and measure the behavior of these databases in different scenarios. The primary focus is on evaluating run time, throughput, and average latency metrics to understand how each database type handles varying thread levels and workload intensity. The outcomes of this research provide valuable insights into the scalability and efficiency aspects of relational databases. By conducting a comprehensive performance comparison, the study aims to assist database designers and developers in selecting the most suitable database option based on specific requirements. The findings contribute to informed decision-making regarding the choice between key-value and relational databases in various data storage scenarios.
- Validating the Use of Smart Glasses in Industrial Quality Control: A Case StudyPublication . Silva, José; Coelho, Pedro; Saraiva, Luzia; Martins, Pedro; López-Rivero, AlfonsoEffective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses’ video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system’s functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems.