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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.
- Agile-based Requirements Engineering for Machine Learning: A Case Study on Personalized NutritionPublication . Cunha, Carlos; Oliveira, Rafael; Duarte, RuiRequirements engineering is crucial in developing machine learning systems, as it establishes the foundation for successful project execution. Nevertheless, incorporating requirements engineering approaches from traditional software engineering into machine learning projects presents new challenges. These challenges arise from replacing the software logic derived from static software specifications with dynamic software logic derived from data. This paper presents a case study exploring an agile requirement engineering approach popular in traditional software projects to specify requirements in machine learning software. These requirements allow reasoning about the correctness of software and design tests for validation. The absence of software specification in machine learning software is offset by employing data quality metrics, which are assessed using cutting-edge methods for model interpretability. A case study on personalized nutrition and physical activity demonstrated the adequacy of user stories and acceptance criteria format, popular in agile projects, for specifying requirements in the machine learning domain.
- Airbnb research: an analysis in tourism and hospitality journalsPublication . Andreu, Luisa; Bigne, Enrique; Amaro, Suzanne; Palomo, JesúsPurpose – The purpose of this study is to examine Airbnb research using bibliometric methods. Using research performance analysis, this study highlights and provides an updated overview of Airbnb research by revealing patterns in journals, papers and most influential authors and countries. Furthermore, it graphically illustrates how research themes have evolved by mapping a co-word analysis and points out potential trends for future research. Design/methodology/approach – The methodological design for this study involves three phases: the document source selection, the definition of the variables to be analyzed and the bibliometric analysis. A statistical multivariate analysis of all the documents’ characteristics was performed with R software. Furthermore, natural language processing techniques were used to analyze all the abstracts and keywords specified in the 129 selected documents. Findings – Results show the genesis and evolution of publications on Airbnb research, scatter of journals and journals’ characteristics, author and productivity characteristics, geographical distribution of the research and content analysis using keywords. Research limitations/implications – Despite Airbnb having a history of 10 years, research publications only started in 2015. Therefore, the bibliometric study includes papers from 2015 to 2019. One of the main limitations is that papers were selected in October of 2019, before the year was over. However, the latest academic publications (in press and earlycite) were included in the analysis. Originality/value – This study analyzed bibliometric set of laws (Price’s, Lotka’s and Bradford’s) to better understand the patterns of the most relevant scientific production regarding Airbnb in tourism and hospitality journals. Using natural language processing techniques, this study analyzes all the abstracts and keywords specified in the selected documents. Results show the evolution of research topics in four periods: 2015-2016, 2017, 2018 and 2019
- An automated closed-loop framework to enforce security policies from anomaly detectionPublication . Henriques, João; Caldeira, Filipe; Cruz, Tiago; Simões, PauloDue to the growing complexity and scale of IT systems, there is an increasing need to automate and streamline routine maintenance and security management procedures, to reduce costs and improve productivity. In the case of security incidents, the implementation and application of response actions require significant efforts from operators and developers in translating policies to code. Even if Machine Learning (ML) models are used to find anomalies, they need to be regularly trained/updated to avoid becoming outdated. In an evolving environment, a ML model with outdated training might put at risk the organization it was supposed to defend. To overcome those issues, in this paper we propose an automated closed-loop process with three stages. The first stage focuses on obtaining the Decision Trees (DT) that classify anomalies. In the second stage, DTs are translated into security Policies as Code based on languages recognized by the Policy Engine (PE). In the last stage, the translated security policies feed the Policy Engines that enforce them by converting them into specific instruction sets. We also demonstrate the feasibility of the proposed framework, by presenting an example that encompasses the three stages of the closed-loop process. The proposed framework may integrate a broad spectrum of domains and use cases, being able for instance to support the decide and the act stages of the ETSI Zero-touch Network & Service Management (ZSM) framework.
- Analysis of the factors that influence the success of relationship marketing in academic libraries.Publication . Figueiredo, Elisabeth; Pereira, Paulo; Ribeiro, Célia; Passos, Clotilde; Antunes, JoaquimPurpose: This study aims to identify the main dimensions of relationship marketing that have the greatest impact on users’ satisfaction and loyalty in academic libraries. Design/methodology/approach: A quantitative study was conducted. Data were collected through a questionnaire survey, directed to the users of four libraries of Universidade Católica Portuguesa, which resulted a total of 292 valid responses. The necessary sample size for the completion of this study was calculated based on Daniel's calculator Soper. Statistical Package for Social Sciences (SPSS 27.0) software and structural equation methods were use to test the proposed model. Originality: This study contributes to the existing literature by examining the specific dimensions of relationship marketing that are most influential in the context of university libraries. While prior research has explored relationship marketing in various contexts, this study focuses on the unique environment of academic libraries, thereby providing novel insights into enhancing user satisfaction and fostering loyalty among library users. Findings: The results show that the relationship marketing dimensions that contribute the most to users’ satisfaction and loyalty are: "relational behaviour", "understanding needs" and "quality of services". Additionally, the study identifies the positive relationship between user satisfaction and loyalty, emphasizing the critical role of relationship marketing in fostering long-term user loyalty in the academic library setting. Theoretical/methodological contributions: Increase of scientific knowledge through the validation of a relationship marketing model, through which its implications for the management of university libraries are evidenced. Practical implications: Understanding the main dimensions of relationship marketing that impact user satisfaction and loyalty allows library managers to adapt their strategies and services to motivate and attract users to their spaces and to better meet their needs.
- Automated Reusable Tests for Mitigating Secure Pattern Interpretation ErrorsPublication . Cunha, Carlos; Pombo, NunoThe importance of software security has increased along with the number and severity of incidents in recent years. Security is a multidisciplinary aspect of the software development lifecycle, operation, and user utilization. Being a complex and specialized area of software engineering, it is often sidestepped in software development methodologies and processes. We address software security at the design level by adopting design patterns that encapsulate reusable solutions for recurring security problems. Design patterns can help development teams implement the best-proven solutions for a specialized problem domain. However, from the analysis of three secure pattern implementations by 70 junior programmers, we detected several structural errors resulting from their interpretation. We propose reusable unit testing test cases based on annotations to avoid secure pattern interpretation errors and provide an example for one popular secure pattern. Providing these test cases to the same group of programmers, they implemented the pattern without errors. The reason is annotations build a framework that disciplines programmers to incorporate secure patterns in their applications and ensure automatic testing.
- Bank Credit and Trade Credit: The Case of Portuguese SMEs from 2010 to 2019Publication . Pinto, Pedro; Henriques, Carla; Cardoso, Carolina Esteves Oliveira da Silva; Neves, Maria Elisabete DuarteSmall companies face significant difficulties in accessing finance, and the use of bank credit and trade credit are the primary sources of financing, specifically in small countries, with little market liquidity, and focused on the banking system, as is the case of Portugal. The main objective of this article is to identify significant drivers of bank and trade credit, as well as investigate the complementary or substitutive relationship between them, considering that both constitute an essential source of financing for small and medium-sized enterprises (SMEs). The sample comprises 5860 companies, and the analysis was performed using panel data methodology (2010–2019). The results suggest that, during the period in which the financial crisis was most felt in the country (2010–2013), companies intensified their demand for trade credit, and in the following years for bank credit. Our evidence does support the substitution hypothesis between trade and bank credit.
- Bank Market Power, Firm Performance, Financing Costs and Capital StructurePublication . Gonçalves, Marisa Pessoa; Reis, Pedro; Pinto, PedroIn this study, we provide a thorough analysis, conducted on a company-by-company basis, of the impact of bank concentration and the bank-relative power of banks on firm profitability, financing costs, and capital structure in a small economy like Portugal. Using a sample of 434,990 Portuguese companies, the study spans a time frame of 13 years (from 2006 to 2018). Principal component analysis (PCA) was used to determine bank concentration, and a new variable, “bank-related power”,was introduced. This work employed linear regression with static panel data for fixed and pooled effects, using Driscoll–Kraay standard errors and robust standard error estimation. A direct associ ation was found between business performance and the use of bank credit in highly concentrated banking markets (SMEs), and there is evidence of an inverse relationship when the relative power of banks increases (small business). Evidence also shows that financing costs increase with greater bank concentration, while firms’ capital structure improves under similar conditions. When a bank holds greater relative market power, it tends to exert a negative impact on the capital structure of large companies. However, an inverse relationship is observed in the case of SMEs. Unlike previous studies, the article assesses the effects of bank market power on each of the different companies involved by using both bank concentration (as a composite variable) and a new variable that measures the relative power of banks. Due to its extensive database and expanded time frame, this research is innovative in the context of small-sized companies.
- Bibliometric Analysis of Studies on Industry 4.0 Maturity Assessment in SMEsPublication . Guimarães, André; Reis, Pedro; Antonio J. Marques Cardoso; Autor correspondente: Guimarães, A..Introdução: Esta pesquisa pretende contribuir para a organização e análise da literatura científica relacionada com a Avaliação do Nível de Maturidade Digital da Indústria 4.0 (I4.0) em pequenas e médias empresas (PMEs). Destaca-se a relevância contínua da transformação digital, impactando as PMEs e oferecendo oportunidades de integração na economia global. Objetivo: O objetivo principal é utilizar técnicas bibliométricas para analisar e organizar a literatura científica disponível na avaliação do Nível de Maturidade Digital da I4.0 em PMEs. Pretende-se contribuir para compreender as tendências de pesquisa, identificar lacunas de conhecimento e fornecer orientações para futuras investigações. Métodos: Realização de uma revisão abrangente da literatura, abrangendo artigos publicados entre 2011 e 2023 nas plataformas Web of Science (WoS) e SciVerse Scopus (Scopus), pela forte reputação, extenso conteúdo e citações globais. Utilização de técnicas bibliométricas facilitadas pelo VOSviewer e pelo software R-studio´s Bibliometrix R para processamento e análise de dados. Resultados: A análise da literatura revelou insights significativos, incluindo a escassez de pesquisas recentes sobre a avaliação do nível de maturidade digital de PMEs no contexto da I4.0. Identificação de tendências de pesquisa, artigos notáveis com base em citações e publicações, bem como reconhecimento de autores frequentemente citados. Conclusão: A importância do estudo reside na análise minuciosa da literatura existente, na avaliação de tendências de pesquisa chave e na identificação de lacunas, fornecendo insights valiosos. As direções propostas e as prioridades para futuras pesquisas destacam a necessidade de investigações adicionais sobre o nível de maturidade digital das PMEs no contexto da I4.0 e áreas como avaliação de desempenho e competências de gestão.
- Bibliometric Analysis of Studies on Lean Maturity Assessment in SMEsPublication . Guimarães, André; Reis, Pedro; Antonio J. Marques Cardoso; Autor correspondente: Guimarães, A..Introdução: Os princípios Lean têm contribuído significativamente para a eficiência e competitividade das pequenas e médias empresas (PMEs). Contudo, persistem lacunas na literatura científica, particularmente na avaliação da implementação do Lean, nos seus impactos no desempenho e na análise das competências de gestão e conhecimento relacionados. Objetivo: Identificar e analisar as tendências de investigação sobre os princípios Lean e a maturidade Lean em PMEs, avaliando as principais contribuições científicas, os artigos mais citados, os autores mais influentes e as lacunas de investigação que possam orientar trabalhos futuros. Métodos: Foi realizada uma análise bibliométrica de artigos publicados entre 2010 e 2024, disponíveis nas bases Web of Science e Scopus. A análise foi suportada pelos softwares Bibliometrix, em R Studio, e VOSviewer, permitindo a visualização de redes de citações, coautorias e palavras-chave. Resultados: São evidenciadas tendências emergentes e lacunas significativas na investigação sobre maturidade Lean em PMEs. Foram identificados artigos, autores e tópicos de maior destaque, bem como a necessidade de mais estudos sobre a avaliação da maturidade Lean e os seus impactos nas práticas organizacionais e no desempenho das PMEs. Conclusão: Este estudo fornece uma visão abrangente do estado da arte sobre os princípios Lean e a maturidade Lean em PMEs. Ao identificar lacunas e propor direções futuras de investigação, contribui para o avanço das práticas Lean, promovendo uma gestão mais eficiente e competitiva.