Browsing by Author "Martins, Pedro"
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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 Evaluation of How Big-Data and Data Warehouses Improve Business Intelligence Decision MakingPublication . Martins, Anthony; Martins, Pedro; Caldeira, Filipe; Sá, Filipe; Rocha, {\'AAnalyze and understand how to combine data warehouse with business intelligence tools, and other useful information or tools to visualize KPIs are critical factors in achieving the goal of raising competencies and business results of an organization. This article reviews data warehouse concepts and their appropriate use in business intelligence projects with a focus on large amounts of information. Nowadays, data volume is more significant and critical, and proper data analysis is essential for a successful project. From importing data to displaying results, there are crucial tasks such as extracting information, transforming it analyzing, and storing data for later querying. This work contributes with the proposition of a Big Data Business Intelligence architecture for an efficiently BI platform and the explanation of each step in creating a Data Warehouse and how data transformation is designed to provide useful and valuable information. To make valuable information useful, Business Intelligence tools are presented and evaluates, contributing to the continuous improvement of business results.
- An Intelligent and Scalable IoT Monitoring Framework for Safety in Civil Construction WorkspacesPublication . Ferreira, Carolina; Correia, Luciano; Lopes, Manuel; Henriques, João; Martins, Pedro; Wanzeller, Cristina; Caldeira, FilipeKeeping civil construction workers safe is an important challenge due to working conditions and low technological support due to the inherent costs. This work surveys the literature and proposes a scalable framework for monitoring workers to minimize the response time with real-time warnings in hazardous situations or safety incidents. From the literature, it was possible to devise a gap in business addressing this problem. To address this problem, this work proposes an IoT scalable framework able to scale to a large number of civil construction companies with a large number of workers in order. The results from this work demonstrate the feasibility of the proposed framework and the low cost of the IoT solution and the scalability of the framework offers the opportunity to leverage new innovative business models capable to leverage their revenues.
- An Overview on Cloud Services for Human TrackingPublication . Martins, Manuel; Mota, David; Martins, Pedro; Abbasi, Maryam; Caldeira, FilipeThis paper reflects the intention to test the use of public cloud services to assess the presence of humans in a given space, more precisely, multiple stores, with the least effort and in the fastest way. It is also intended to demonstrate that the use of the public cloud can be an instrument of added value in business areas and research areas. In the specific case, the cloud was used to train and use artificial intelligence models.
- An overview on how to develop a low-code application using OutSystemsPublication . Martins, Ricardo; Caldeira, Filipe; Sá, Filipe; Abbasi, Maryam; Martins, PedroThe motivation for developing a self-service platform for employees arises precisely from the idea that in all organizations there are tasks that could be automated in order to redirect work resources to more important tasks. The proposed application consists of the development of a self-service platform, for personal information and scheduling tasks, aimed at the employees instead of all the solutions that are in the market that aim their platform to the Human Resources. We focus on the employers giving them more responsibility to make their own personal management like, change their personal info, book their vacations and other, giving to the Human Resources the tasks of managing all these actions made by the employers. At the end of the work, it is expected that the final solution to be considered as an example of success with regards to the theme of business automation and innovation, using the low-code application Outsystems to perform the full proposed application development.
- Beacons positioning detection, a novel approachPublication . Morgado, Francisco; Martins, Pedro; Caldeira, FilipeRecent Bluetooth Low Energy (BLE) beacons provide new opportunities to explore positioning. Beacon positioning determination using current approaches is supported by pre-calculated formulas, for generic beacons, whereas the position can be accurately estimated with a low error up to a small distance; or based on fingerprinting the signal for the given space. In both cases, the accuracy variate depending on hardware specifications and other conditions such as beacon brand, wrap material, temperature, wind, location, surrounding interference, battery strength, among others. This paper introduces a method for beacon-based positioning, based on signal strength measurements at key distances for each beacon. This method allows for different beacon types, brands, and conditions. Depending on each situation (i.e., hardware and location) it is possible to adapt the distance measuring curve to minimize errors and support higher distances, while at the same time keeping good precision. Moreover, this paper also presents a comparison with traditional positioning method, using formulas for distance estimation, and then position triangulation. Performed tests took place at the library of the campus of the Polytechnic Institute of Viseu. Experimental results show that the proposed position technique has 13.2% better precision than triangulation, for distances up to 10 meters.
- Cisco NFV on Red Hat OpenStack PlatformPublication . Oliveira, Luis; Martins, Pedro; Abbasi, Maryam; Caldeira, FilipeThe traditional telecom networks have been facing constant challenges to keep up with bandwidth growth, latency, data consumption and coverage. On top of it, there are also new use cases of telecom infrastructures usage such as the IoT exponential growth. The Network Function Virtualization (NFV) appears as the solution for the transition between high-cost dedicated hardware to low-cost commercial off-theshelf (COTS) servers. This transition will not only meet the requirements of the new telecom reality but also reduces the overall operational cost of the network. This document illustrates the implementation of Cisco Virtual Network Functions (VNFs) of a vEPC on top of Red Hat OpenStack Platform.
- Cityaction a smart-city platform architecturePublication . Martins, Pedro; Albuquerque, Daniel; Wanzeller, Cristina; Caldeira, Filipe; Tomé, P.; Sá, FilipeFast population growth in cities and surrounding regions force cities to become smarter to have a sustainable economy, social quality, and environmental well-being. Smart-Cities will be the ones using information and communication technologies to make cities services more efficient (in performance and cost), interactive, and aware of events. For a city to become smarter, it needs to make use of emerging technologies related with Internet-of-Things (IoT), not only to collect information and interact (actuate, command, control) but also to provide services for analytics and other applications. In this paper, is researched the concept of smart-city in the context of the project CityAction, tested on the city of Castelo Branco, Portugal. This project focuses on the relationship between IoT, monitoring, actuating and displaying data. Based on collected data from sensors spread across the city, the proposed project aims to make “smart” decisions to optimize resources, cost, well living, and environmental impact. Results introduce an architecture to integrate multiple heterogeneous sensors, develop a dashboard able of displaying data in a user-friendly way, and making this information available to population and users through a mobile app. This mechanism makes possible to infer better decisions on the city management/behavior and put in place the needed mechanisms to improve response time, safety and well living.
- Comprehensive Evaluation of Deepfake Detection Models: Accuracy, Generalization, and Resilience to Adversarial AttacksPublication . Abbasi, Maryam; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, PedroThe rise of deepfakes—synthetic media generated using artificial intelli gence—threatens digital content authenticity, facilitating misinformation and manipu lation. However, deepfakes can also depict real or entirely fictitious individuals, leveraging state-of-the-art techniques such as generative adversarial networks (GANs) and emerging diffusion-based models. Existing detection methods face challenges with generalization across datasets and vulnerability to adversarial attacks. This study focuses on subsets of frames extracted from the DeepFake Detection Challenge (DFDC) and FaceForensics++ videos to evaluate three convolutional neural network architectures—XCeption, ResNet, and VGG16—for deepfake detection. Performance metrics include accuracy, precision, F1-score, AUC-ROC, and Matthews Correlation Coefficient (MCC), combined with an assessment of resilience to adversarial perturbations via the Fast Gradient Sign Method (FGSM). Among the tested models, XCeption achieves the highest accuracy (89.2% on DFDC), strong generalization, and real-time suitability, while VGG16 excels in precision and ResNet provides faster inference. However, all models exhibit reduced performance under adversarial conditions, underscoring the need for enhanced resilience. These find ings indicate that robust detection systems must consider advanced generative approaches, adversarial defenses, and cross-dataset adaptation to effectively counter evolving deep fake threats
- 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
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