Instituto Politécnico de Viseu
Repositório Científico
Entradas recentes
Aplicação do Lean Seis Sigma no Setor do Turismo: Um estudo de caso na Indústria Hoteleira Portuguesa
Publication . ANTUNES VAZ, PAULO JOAQUIM; Rocha, Sílvia; Reis, Marco S.; Silva, Cristóvão; Vaz, Maria
Num ambiente cada vez mais competitivo, fatores como a eficiência, a qualidade de produtos, processos e serviços, assim como a satisfação de clientes, são, cada vez mais determinantes para o sucesso de qualquer organização. Para atingir estas vantagens competitivas, iniciativas de Qualidade como o Lean e o Seis Sigma têm revelado ser importantes estratégias de negócio em muitas indústrias em todo o mundo. No entanto, apesar da sua aplicabilidade ser praticamente transversal a todos os setores da economia, estas metodologias ainda não despertaram, tanto quanto se sabe, um interesse generalizado no setor do Turismo em Portugal. No sentido de colmatar esta lacuna, este trabalho procura avaliar a oportunidade de implementar estas metodologias de qualidade no setor do Turismo, mais concretamente na Hotelaria Portuguesa.
Foi feito um levantamento das práticas correntes de implementação de sistemas de qualidade na Hotelaria Portuguesa. Adicionalmente, foi ainda implementada a iniciativa Lean Seis Sigma a um estabelecimento Hoteleiro Nacional, recorrendo à metodologia do Seis Sigma para a resolução de problemas – DMAIC (Define, Measure, Analyse, Improve e Control), tendo sido realizadas as primeiras três etapas e elencadas propostas de melhorias a implementar na fase seguinte.
Este trabalho permite analisar a pertinência da implementação do Lean Seis Sigma na Hotelaria Portuguesa, bem como avançar com a sua aplicação a um caso prático.
An integrated and interoperable AutomationML-based platform for the robotic process of metal additive manufacturing
Publication . Babcinschi, Mihail; Freire, Bernardo; Ferreira, Lucía; Señaris, Baltasar; Vidal, Felix; Vaz, Paulo; Neto, Pedro
Increasingly, 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.
Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches
Publication . Abbasi, Maryam; Bernardo, Marco V.; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, Pedro
While 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 Strategies
Publication . Abbasi, Maryam; Bernardo, Marco V.; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Martins, Pedro
Complex 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.
Impact of Cyberbullying on Academic Performance and Psychosocial Well-Being of Italian Students
Publication . Ragusa, Antonio; Núñez-Rodríguez, Sandra; ANTUNES VAZ, PAULO JOAQUIM; Silva, José; Caliciotti, Virginia; González-Bernal, Jerónimo J.; López-Rivero, Alfonso J.; Petrillo, Ema; Gatto, Manuela; Obregón-Cuesta, Ana Isabel; González-Santos, Josefa
Cyberbullying is a growing problem in the Italian educational sector, with a prevalence of 17%. This study analyzes its impact on the psychosocial well-being and academic performance of Italian adolescents. Method: A cross-sectional study was conducted with 502 students from six schools in different Italian regions, using the European Cyberbullying Intervention Project Questionnaire (ECIPQ) to assess cyberbullying, in addition to collecting data on satisfaction, friends, and academic performance. Chi-square and ANOVA analyses were conducted to identify significant associations between the variables. Results: The analyses showed significant associations between cyberbullying and gender and in psychosocial well-being, with significant differences in personal satisfaction and body satisfaction. On the other hand, there were no significant differences in academic performance or in the ability to make new friends, although victims showed a significantly lower ability to make new friends compared to those who were neither victims nor aggressors. Conclusions: Cyberbullying has a significant impact on students’ psychosocial well-being, especially on personal satisfaction and school happiness, making it essential to implement interventions that promote safe school environments to mitigate these negative effects.