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- Unified Data Governance in Heterogeneous Database Environments: An API-Driven Architecture for Multi-Platform Policy EnforcementPublication . Abbasi, Maryam; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Cardoso, Filipe; Sá, Filipe; Martins, Pedro; Cardoso, Filipe; Sá, Filipe; Martins, PedroModern organizations increasingly rely on heterogeneous database environments that combine relational, document-oriented, and key-value storage systems to optimize performance for diverse application requirements. However, this technological diversity creates significant challenges for implementing consistent data governance policies, regulatory compliance, and access control across disparate systems. Traditional governance approaches that operate within individual database silos fail to provide unified policy enforcement and create compliance gaps that expose organizations to regulatory and operational risks. This paper presents a novel API-driven architecture that enables unified data governance across heterogeneous database environments without requiring database-specific modifications or vendor lock-in. The proposed framework implements a centralized governance layer that coordinates policy enforcement across PostgreSQL, MongoDB, and Amazon DynamoDB systems through RESTful API interfaces. Key architectural components include differentiated access control through hierarchical API key management, automated compliance workflows for regulatory requirements such as GDPR, real-time audit trail generation, and comprehensive data quality monitoring with automated improvement mechanisms. Comprehensive experimental evaluation demonstrates the framework’s effectiveness across multiple operational dimensions. The system achieved 95.2% accuracy in access control enforcement across different data classification levels, while automated GDPR compliance workflows demonstrated 98.6% success rates with average processing times of 2.9 h. Performance evaluation reveals acceptable overhead characteristics with linear scaling patterns for PostgreSQL operations (R2 = 0.89), consistent sub-20ms response times for MongoDB logging operations, and sustained throughput rates ranging from 38.9 to 142.7 requests per second across the integrated system. Data quality improvements ranged from 16.1% to 34.3% across accuracy, completeness, consistency, and timeliness dimensions over a 12-week monitoring period, with accuracy improving by 17.8 percentage points, completeness by 13.2 percentage points, consistency by 19.7 percentage points, and timeliness by 24.5 percentage points. The duplicate detection system achieved 94.6% precision and 95.6% recall across various duplicate types, including cross-database redundancy identification. The results demonstrate that API-driven governance architectures can effectively address the persistent challenges of policy fragmentation in multi-database environments while maintaining operational performance and enabling measurable improvements in data quality and regulatory compliance. The framework provides a practical migration path for organizations seeking to implement comprehensive governance capabilities without replacing existing database infrastructure investments.
- Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel ApproachesPublication . Abbasi, Maryam; Bernardo, Marco V.; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, PedroWhile the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability.
- Optimizing Database Performance in Complex Event Processing through Indexing StrategiesPublication . Abbasi, Maryam; Bernardo, Marco V.; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, PedroComplex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data continue to grow, optimizing the performance of CEP systems becomes a critical challenge. This paper investigates the impact of indexing strategies on the performance of databases handling complex event processing. We propose a novel indexing technique, called Hierarchical Temporal Indexing (HTI), specifically designed for the efficient processing of complex event queries. HTI leverages the temporal nature of event data and employs a multi-level indexing approach to optimize query execution. By combining temporal indexing with spatial- and attribute-based indexing, HTI aims to accelerate the retrieval and processing of relevant events, thereby improving overall query performance. In this study, we evaluate the effectiveness of HTI by implementing complex event queries on various CEP systems with different indexing strategies. We conduct a comprehensive performance analysis, measuring the query execution times and resource utilization (CPU, memory, etc.), and analyzing the execution plans and query optimization techniques employed by each system. Our experimental results demonstrate that the proposed HTI indexing strategy outperforms traditional indexing approaches, particularly for complex event queries involving temporal constraints and multi-dimensional event attributes. We provide insights into the strengths and weaknesses of each indexing strategy, identifying the factors that influence performance, such as data volume, query complexity, and event characteristics. Furthermore, we discuss the implications of our findings for the design and optimization of CEP systems, offering recommendations for indexing strategy selection based on the specific requirements and workload characteristics. Finally, we outline the potential limitations of our study and suggest future research directions in this domain.
- Environmental and Economic Assessment of Desktop vs. Laptop Computers: A Life Cycle ApproachPublication . Domingos Ferreira, Miguel; Domingos, idalina; Leite dos Santos, Lenise Maria; Barreto Ana; Ferreira, JoséThis study evaluates and compares the environmental and economic implications of desktop and laptop computer systems throughout their life cycles using screening life cycle assessment (LCA) and life cycle costing (LCC) methodologies. The functional unit was defined as the use of one computer system for fundamental home and small-business productivity tasks for over four years. The analysis considered the production, use, and end-of-life phases. The results showed the desktop system had a higher overall carbon footprint (679.1 kg CO2eq) compared to the laptop (286.1 kg CO2eq). For both systems, manufacturing contributed the largest share of the emissions, followed by use. Desktops exhibited significantly higher use phase emissions, due to greater energy consumption. Life cycle cost analysis revealed that laptops had slightly lower total costs (EUR 593.88) than desktops (EUR 608.40) over the 4-year period, despite higher initial investment costs. Sensitivity analysis examining different geographical scenarios highlighted the importance of considering regional factors in the LCA. Manufacturer-provided data generally showed lower carbon footprint values than the modeled scenarios. This study emphasizes the need for updated life cycle inventory data and energy efficiency improvements to reduce the environmental impacts of computer systems. Overall, laptops demonstrated environmental and economic advantages over desktops in the defined usage cases.
- Olive Tree (Olea europaea) Pruning: Chemical Composition and Valorization of Wastes Through LiquefactionPublication . Domingos, idalina; Domingos Ferreira, Miguel; Ferreira, José; Esteves, Bruno; MDPIOlive tree branches (OB) and leaves (OL) from the Viseu region (Portugal) were studied for their chemical composition and liquefaction behavior using polyalcohols. Chemical analysis revealed that OL contained higher ash content (4.08%) and extractives, indicating more bioactive compounds, while OB had greater α-cellulose (30.47%) and hemicellulose (27.88%). Lignin content was higher in OL (21.64%) than OB (16.40%). Liquefaction experiments showed that increasing the temperature from 140 ◦C to 180 ◦C improved conversion, with OB showing a larger increase (52.5% to 80.9%) compared to OL (66% to 72%). OB reached peak conversion faster, and the optimal particle size for OB was 40–60 mesh, while OL performed better at finer sizes. OL benefited more from higher solvent ratios, whereas OB achieved high conversion with less solvent. FTIR analysis confirmed that acid-catalyzed liquefaction breaks down lignocellulosic structures, depolymerizes cellulose and hemicellulose, and modifies lignin, forming hydroxyl, aliphatic, and carbonyl groups. These changes reflect progressive biomass degradation and the incorporation of polyalcohol components, converting solid biomass into a reactive, polyol-rich liquid. The study highlights the distinct chemical and processing characteristics of olive branches and leaves, informing their potential industrial applications.
- Life Cycle Assessment of Pig Production in Central Portugal: Environmental Impacts and Sustainability ChallengesPublication . Leite dos Santos, Lenise Maria; Domingos Ferreira, Miguel; Domingos, idalina; Oliveira Verónica; Rodrigues Carla; Ferreira António; Ferreira, José; MDPIPig farming plays a crucial socioeconomic role in the European Union, which is one of the largest pork exporters in the world. In Portugal, pig farming plays a key role in regional development and the national economy. To ensure future sustainability and minimize environmental impacts, it is essential to identify the most deleterious pig production activities. This study carried out a life cycle assessment (LCA) of pig production using a conventional system in central Portugal to identify the unitary processes with the greatest environmental impact problems. LCA followed the ISO 14040/14044 standards, covering the entire production cycle, from feed manufacturing to waste management, using 1 kg of live pig weight as the functional unit. The slurry produced is used as fertilizer in agriculture, replacing synthetic chemical fertilizers. Results show that feed production, raising piglets, and fattening pigs are the most impactful phases of the pig production cycle. Fodder production is the stage with the greatest impact, accounting for approximately 60% to 70% of the impact in the categories analyzed in most cases. The environmental categories with the highest impacts were freshwater ecotoxicity, human carcinogenic toxicity, and marine ecotoxicity; the most significant impacts were observed for human health, with an estimated effect of around 0.00045 habitants equivalent (Hab.eq) after normalization. The use of more sustainable ingredients and the optimization of feed efficiency are effective strategies for promoting sustainability in the pig farming sector.
- Suggestions for promoting SOC storage within the carbon farming framework: Analyzing the INFOSOLO databasePublication . Cunha, Carlos; Castanheira, Nádia Luísa; Ramos, Tiago Brito; Martinho, Vítor João Pereira Domingues; Ferreira, António José Dinis; Pereira, José Luís da Silva; Sánchez-Carreira, Maria del CarmenThe new world challenges under climate change call for eco-friendly practices that make agriculture’s economic and social dimensions compatible with environmental preservation and ecosystem resilience. Carbon farming has emerged as an interesting alternative for dealing with these new frameworks, as it promotes conservation agriculture with practices that increase carbon sequestration in soils and plants. Considering these motivations, this research intends to bring more insights into the levels of soil organic carbon (SOC) in the Portuguese context, and this variable is interrelated with land use, land attributes, and soil characteristics. Statistical information from the INFOSOLO legacy database was analyzed through statistical methodologies and machine-learning approaches. The findings provide interesting support for the stakeholders about the influence of land use and soil types on the levels of SOC.
- 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.
- 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.
- Machine Learning Approaches for Predicting Maize Biomass Yield: Leveraging Feature Engineering and Comprehensive Data IntegrationPublication . Abbasi, Maryam; Vaz, Paulo; Silva, José; Martins, Pedro; Silva, José; ANTUNES VAZ, PAULO JOAQUIMThe efficient prediction of corn biomass yield is critical for optimizing crop production and addressing global challenges in sustainable agriculture and renewable energy. This study employs advanced machine learning techniques, including Gradient Boosting Machines (GBMs), Random Forests (RFs), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), integrated with comprehensive environmental, soil, and crop management data from key agricultural regions in the United States. A novel framework combines feature engineering, such as the creation of a Soil Fertility Index (SFI) and Growing Degree Days (GDDs), and the incorporation of interaction terms to address complex non-linear relationships between input variables and biomass yield. We conduct extensive sensitivity analysis and employ SHAP (SHapley Additive exPlanations) values to enhance model interpretability, identifying SFI, GDDs, and cumulative rainfall as the most influential features driving yield outcomes. Our findings highlight significant synergies among these variables, emphasizing their critical role in rural environmental governance and precision agriculture. Furthermore, an ensemble approach combining GBMs, RFs, and ANNs outperformed individual models, achieving an RMSE of 0.80 t/ha and R2 of 0.89. These results underscore the potential of hybrid modeling for real-world applications in sustainable farming practices. Addressing the concerns of passive farmer participation, we propose targeted incentives, education, and institutional support mechanisms to enhance stakeholder collaboration in rural environmental governance. While the models assume rational decision-making, the inclusion of cultural and political factors warrants further investigation to improve the robustness of the framework. Additionally, a map of the study region and improved visualizations of feature importance enhance the clarity and relevance of our findings. This research contributes to the growing body of knowledge on predictive modeling in agriculture, combining theoretical rigor with practical insights to support policymakers and stakeholders in optimizing resource use and addressing environ mental challenges. By improving the interpretability and applicability of machine learning models, this study provides actionable strategies for enhancing crop yield predictions and advancing rural environmental governance.