Companion Proceedings of the Brazilian Symposium on Information Systems (SBSI)
Sociedade Brasileira de Computação (SBC)
This document brings the front matter of the Companion Proceedings of SBSI 2025.
A comprehensive exploitation of instance selection methods for automatic text classification
Progress in Natural Language Processing (NLP) has been dictated by the rule of more: more data, more computing power and more complexity, best exemplified by the Large Language Models. However, training (or fine-tuning) large dense models for specific applications usually requires significant amounts of computing resources. This Ph.D. dissertation focuses on an under-investigated NLP data engineering technique, whose potential is enormous in the current scenario known as Instance Selection (IS). The IS goal is to reduce the training set size by removing noisy or redundant instances while maintaining the effectiveness of the trained models and reducing the training process cost. We provide a comprehensive and scientifically sound comparison of IS methods applied to an essential NLP task – Automatic Text Classification (ATC), considering several classification solutions and many datasets. Our findings reveal a significant untapped potential for IS solutions. We also propose two novel IS solutions that are noise-oriented and redundancy-aware, specifically designed for large datasets and transformer architectures. Our final solution achieved an average reduction of 41% in training sets, while maintaining the same levels of effectiveness in all datasets. Importantly, our solutions demonstrated speedup improvements of 1.67x (up to 2.46x), making them scalable for datasets with hundreds of thousands of documents. This thesis falls under the Information Systems and Artificial Intelligence (Generative, LLM, NLP, among others) topic of the SBSI-CTDGSI call for papers.
A Method to Support Requirements Change Management in Software Ecosystems Based on Open Innovation and CrowdRE
Ecossistemas de software (ECOS) tornam a gerência de mudanças de requisitos mais complexa devido à sua natureza dinâmica e colaborativa. Este trabalho propõe o método SECO-RCI, que auxilia na identificação de mudanças de requisitos em ECOS, combinando inovação aberta e feedback da multidão. O método foi desenvolvido com base na Design Science, sua aceitação foi avaliada por meio de um grupo focal e sua viabilidade por meio de um estudo de caso em um ECOS real. Os resultados indicam uma aceitação positiva e a necessidade de suporte ferramental para sua aplicação prática. O método SECO-RCI contribui para a compreensão dos ECOS e se alinha aos pilares dos sistemas de informação: pessoas (multidão), processos (gerência de mudanças de requisitos) e tecnologia (ECOS).
A meta-scientific study of ethics through networks and the brazilian computational academic-scientific culture
This research investigates computational ethics and research ethics in the Brazilian academic-scientific context of computing, highlighting the growing relevance of ethical and moral issues in the face of technological advances. The work is divided into two parts, one a systematic review and the other an interaction with experts, through questionnaire and interviews. There is a scenario of scarcity and deficiency of ethics, although the importance of ethics is recognized. I proposed actions to foster ethical or moral advancement, such as greater involvement of the SBC, development of instructional materials and integration with CEPs. Ignorance and lack of education are the main obstacles.
A Participation Model for Urban Planning in Small and Medium-Sized Cities
The expansion of digital technologies, such as the internet and social media, has increased opportunities for citizen participation. However, integrating these technologies into participatory processes still faces challenges. To address these barriers, this research, based on Design Science Research, proposes a model to support the development of engagement strategies in urban planning for small and medium-sized cities. Developed and refined over three iterative cycles, the model was evaluated through qualitative analysis and real-world applications. The results demonstrate its replicability and adaptability to different urban contexts, providing insights to enhance public policies.
Sociotechnical Vision of Education in Information Systems: Equity and Self-Directed Learning from the Global South
Information System Education (ISE) concerns the reflection of equitable variables like gender, race/ethnicity, socioeconomic status and culture. However, addressing how to balance different sources of inequities is still an open challenge. Active learning and Self-Directed Learning (SDL) have the potential to create a more equitable educational environment, promoting more autonomy and crucial soft skills for students in our complex society. Understanding better how SDL effectively occurs in ISE students can also contribute to comprehending the potential of active learning in terms of capabilities approach (CA), a solid equity theory. Thus, this doctoral research investigated how ISE students conduct their SDL in developing countries from the CA lens. The results are structured over the perceptions of two ISE Brazilian undergraduates about their SDL trajectories, being each one from the lowest and highest socioeconomic status of their class, respectively. Interviews and other data sources helped to better situate the findings. The doctoral contributions were in (i) the use of CA as an equity theoretical framework in computing research, and computing education (including ISE), (ii) the proposition of a new concept called SDL capabilities, and (iii) a pragmatic instantiation of equity discussions in ISE (beyond other scientific contributions to Computing and Education in a general way).
A Scenario-Based Approach for Fault Tolerance Requirements Extraction in Systems-of-Systems from Business Process-of-Processes
Falhas podem ocorrer durante a interoperabilidade de Sistemas-de-Sistemas (SoS) afetando o seu funcionamento e confiabilidade. Este trabalho define uma abordagem baseada em cenários na notação BPMN (Business Process Model and Notation) para extrair, de maneira sistemática e automática, requisitos de tolerância a falhas de SoS durante a interoperabilidade entre os seus sistemas constituintes, a partir de informação útil de Processos-de-Processos de Negócio (PoP) de alianças de organizações. Com isso, é possível alcançar alinhamento entre os níveis técnico e de negócio, podendo favorecer a competitividade e lucratividade de alianças de organizações.
A Conceptual Model for Describing and Classifying Interoperability Types - Extended Abstract – CTDGSI 2026
Interoperability is critical to modern information systems, enabling collaboration and data exchange across different software and domains. This master’s thesis reviews 37 secondary studies (2012–2023), identifying 36 types of interoperability, 117 definitions, 13 models, and six frameworks, highlighting a shift from technical to socio-technical issues. To address challenges related to this requirement fulliment, a conceptual model with two diagrams was proposed and evaluated, achieving 87.9% acceptance for the classification diagram and 78.8% for the description diagram. The research aims to improve the understanding of interoperability requirements and contribute to the challenges of the GrandSI-BR 2016–2026 roadmap.
A self-adaptive IoT architecture to support computational resource allocation in an e-health environment Extended Abstract – CTDSI/2025
Intelligent environments are complex interaction spaces between people, sensors, devices, and systems. Software Engineering must tackle the intrinsic characteristics of devices and sensors and complex interactions in intelligent environments to consolidate development practices. This work aims to present a self-adaptive IoT architecture in an intelligent environment and evaluate an information system developed based on the architecture. The proposal concerns how the architecture modules work to develop new information systems. The work was developed through a real-world case study in an intelligent e-health environment. The results showed how a self-adaptive architecture using artificial intelligence can support the development of an information system to manage intelligent e-health spaces.
CarbOnto: Data Integration Towards Net Zero - Extended Abstract – CTDGSI 2025
This work presents an architecture called CarboFarm, that encompasses an ontology for the syntactic and semantic integration of heterogeneous databases related to the agricultural domain. By utilizing ontology, we contribute to the standardization and interpretation of domain concepts and add semantic information to support the generation of GHG (Greenhouse Gas) inventories on farms. The Artificial Intelligence (AI) component processes integrated data to derive GHG information. The inventories can identify imbalances between emissions and gas stocks, helping search for solutions to neutralize emissions. We conducted a that using CarboFarm can support the balancing of gases.
IT Ambidexterity in Brazilian Federal Public Institutions
Companies have adopted Digital Transformation (DT) journeys to remain competitive in the current market dynamics. IT ambidexterity and IT Governance can help them, but there are no studies addressing the situation in the Brazilian public sector, which has specific cultural aspects and a strong political environment. This study investigates the situation of IT Governance, IT Ambidexterity and DT in Brazilian Federal Public Institutions and proposes a framework that combines IT Ambidexterity archetypes with IT Governance mechanisms; and an instrument for choosing the best model. The result also helps to organize knowledge about IT Governance and IT Ambidexterity, as well as organizations to execute DT initiatives.
Evaluating how different balancing data techniques impact on prediction of premature birth - Extended Abstract – CTDGSI 2025
This paper does not have an abstract.
Data Privacy in Educational Contexts: Analyzing Perceptions, Practices and Challenges in Personal Data Protection - Extended Abstract CTDGSI 2025
This extended abstract presents a study on personal data privacy in educational contexts. Combining a survey and a systematic literature review, it analyzes perceptions, practices, and challenges in protecting student and teacher data. The findings reveal significant gaps in the privacy Brazilian law LGPD (Lei Geral de Proteção de Dados Pessoais) knowledge, inadequate training, and insufficient data security measures across institutions. In addition, discrepancies in data handling practices between professors and students are highlighted, emphasizing the need for clearer policies and improved transparency. Finally, the research contributes suggestions for future work aimed at enhancing regulatory compliance and fostering a culture of privacy in education.
Overcoming Obstacles: Challenges of Gender Inequality in Undergraduate ICT Programs Extended Abstract – CTDGSI 2025
Gender inequality in Information and Communication Technology (ICT) programs is a persistent issue, particularly in Brazil, where women represent less than 18% of ICT students. This study explores the perceptions of 402 women enrolled in ICT undergraduate programs across 18 Brazilian states. Using a mixed-method approach, we identify key challenges such as discriminatory practices, gender stereotypes, and lack of representation, which undermine women’s confidence and participation. Findings reveal that women face interruptions, isolation, and mistrust in academic settings, often leading to course discontinuation. Strategies to improve women’s confidence and retention include increasing representation, fostering supportive environments, and raising awareness about gender biases.
Avalia: AI and ENADE Data-Based Tool for Curriculum Analysis of the Information Systems Program at Faculdade Metodista Granbery
This study presents the "Avalia" tool, which uses a Large Language Model (LLM) to analyze ENADE data for the Information Systems program at Faculdade Metodista Granbery. The tool correlates exam questions, course subjects, and student results to identify knowledge gaps and support curriculum revisions. Data from ENADE 2021 were processed with a hybrid approach combining cosine similarity and LLM techniques, and reports were generated alongside an interactive chatbot for natural language queries. Proof-of-concept results achieved 90.9% accuracy, demonstrating the viability of LLMs for educational data analysis. These findings encourage further adoption of LLM-based tools across academic environments and suggest potential expansion to other programs.
Prediction of Gain or Loss of Function in Missense Variants
Missense variants alter a single amino acid in proteins and can cause loss-of-function (LOF) or gain-of-function (GOF). Accurately classifying these effects is essential for understanding genetic diseases and tailoring precision medicine approaches. Here, we propose the Gain and Loss of Function Dataset (GLOF), the first dataset of LOF, GOF, and Neutral missense variants annotated by specialists, curated with one of Latin America’s largest genetic diagnostics laboratories. Using embeddings from the ESM-1v model, our Random Forest model achieves state-of-the-art results without requiring complex feature engineering or multi-sequence alignment. We hope the GLOF dataset will stimulate further research into LOF/GOF prediction for personalized genomics.
Business Process Automation in the Cultural Sector: An Experience from the Culture Chief Scientist Program
This study presents the Mirante Digital tool, developed under the Cientista Chefe da Cultura program, aimed at automating the administrative and operational processes of the Instituto Mirante. The tool seeks to optimize cultural management by integrating the Institute with the Secretariat of Culture of the State of Ceará. Using an iterative and incremental approach, the project leverages modern technologies and agile methodologies to create digital solutions that enhance efficiency and transparency in the Institute’s operations.
Automatic Quality Evaluation of Complaint Responses in the Brazilian Telephony Sector
Complaint platforms in Brazil influence how consumers look at reviews to make their purchasing decisions, especially in the telecommunications sector, where companies’ responses can attract or alienate customers. Many of these companies face difficulties in providing effective responses, losing competitive advantages. This paper proposes an AI model to classify customer satisfaction based on responses to complaints. Textual representation methods, such as TF-IDF, BoW, and BERT, and machine learning with hyperparameter adjustments were used to optimize the performance of the trained models. The results showed that removing the neutral class from the reviews and adjusting BERT increased accuracy, highlighting its importance in analyzing reviews and improving customer service, with the implementation of an API for commercial use.
Transforming Public Administration: AI-Driven Tools for Summarizing Corruption Cases in Federal Education
The fight against corruption in public institutions, particularly in universities and federal institutes, requires efficient tools for auditing and monitoring administrative processes. This paper presents a tool designed to analyze corruption cases by identifying key elements within these processes, such as classification, damage, and motivation, while observing recurring patterns. The tool leverages Natural Language Processing (NLP) techniques and Large Language Models (LLMs) to automate the extraction and analysis of relevant information. The results demonstrate that the solution can significantly reduce the time required for audits while enhancing accuracy in detecting irregularities.
Memory preservation with digital accessibility: a plugin for image description using generative AI
This article describes the development of a plugin that employs generative Artificial Intelligence (AI) to enhance accessibility in historical archive cataloging systems, particularly for visually impaired users. Developed with generic instructions and a free model from Google, the plugin was tested by regular users, achieving high satisfaction (91.36 on the System Usability Scale). The plugin has been improved based on user feedback and continues to evolve, with plans to refine the instructions and conduct tests with audio description experts and visually impaired individuals.
Smart Digital Platform for R&D Integration in Collaborative Projects Between Industry, Academia and Government
Collaborations between industry, government, and academia have increasingly driven digital transformations, both in software product development and in the research and innovation of new applied technologies. The goal of this initiative is to strengthen these partnerships and promote the integration of these sectors through artificial intelligence, utilizing technologies such as Large Language Models (LLM) and machine learning. Currently, we have two versions of the proposed platform’s MVP, which aims to offer functionalities tailored to different audiences, such as Companies, Researchers, and ICTs.
Colhe Aí: An Application for Irrigation Control and Soil Monitoring System
It is estimated that 70% of all water used in agriculture is wasted due to a lack of control over the amount of water used. Therefore, smart agriculture seeks to help farmers in assessing production conditions. The present work aims to present Colhe aí, an irrigation assistance system for farmers based on an application and an embedded system for managing soil and its resources.
IT Governance Assessment Tool for Public Agencies
Information Technology (IT) governance plays a crucial role in the current digital era, where reliance on systems and data is ubiquitous. It refers to the set of practices and policies ensuring that IT resources are aligned with the strategic objectives of the organization, optimizing processes, mitigating risks, and driving innovation. This work presents a proposal for developing an assessment survey for IT governance. This survey not only aligns IT processes with the strategic needs of public organizations but also facilitates regulatory compliance and operational efficiency. The results suggest that the adoption of COBIT 2019 can significantly enhance IT governance practices, promoting greater transparency, accountability, and innovation in the public sector.
Ecograd: An analytical tool for evidence-based management in Brazilian Federal Institutions of Higher Education
A survey conducted with 65 Brazilian Federal Higher Education Institutions (FHEI) revealed that 73.8% lack a clear strategy for using data in undergraduate management, and 66.2% do not have an adequate platform for visualizing key indicators. To address these challenges, we developed Ecograd, an analytical system that integrates various datasets and provides organized, real-time information for academic managers. Using open data, the platform consists of 27 dashboards covering aspects such as enrollment, dropout rates, graduation, quality indicators, and employability. Ecograd enables benchmarking, institutional comparisons, and the identification of best practices in undergraduate management. Currently, a platform is being used regularly by 54 FHEI.
Ethical Design in Basic Education – Adapting Information Systems to the Needs of Children and Adolescents
The expansion of Big Techs in education fosters a platformization scenario, marked by surveillance practices and data extraction. This study, a partnership between UFRPE and Instituto Alana, proposes an ethical design guide for educational software. The adopted methodology involved phases of gray literature analysis, categorization of practices, and joint refinement with experts. As a result, 78 practices were defined and organized into 10 categories, including governance, data protection, and artificial intelligence. This research aims to strengthen child protection and promote responsible educational solutions in the technology market.
Towards Sustainable Fishing: Enhancing Data Management in the Araguaia-Tocantins Basin with AI Technologies
The Araguaia-Tocantins basin, essential for riverine communities due to its importance in artisanal fishing, faces a substantial lack of information, preventing the formulation of effective policies. Although the Fishery Statistics System (SIEPE) exists as a traditional information system, the proposed integration with Artificial Intelligence (AI) platforms allows the formulation of natural language queries that are converted into Structured Query Language (SQL) commands, thereby increasing the accessibility of the available data. This innovation has the potential to improve the management of artisanal fishing and create new business opportunities, focusing on sustainability and the preservation of natural resources.
Big Data in the Cultural Sector: An Initial Approach to Public Management in Ceará
This work presents an initiative for applying Big Data in cultural management in Ceará. The proposed solution aims to integrate and analyze data from various public sources to support cultural policy-making. The project has conducted a literature review and implemented a structured data collection and processing pipeline. Preliminary results include exploratory analyses and the development of an interactive dashboard for dynamic visualization.
Climate Dataset Analyzer: A Tool to Extract, Process and Compare Data from Climate Models and Stations
The proliferation and new generation of climate models significantly expand the possibilities for analyzing the impacts of climate change, both for the public and private sectors, at regional and local scales. However, given the large number of available models, each with its own biases, selecting the most suitable model for reference in a research project or initiative becomes a challenging task. In this context, the developed tool Climate Dataset Analyzer (CDA) emerges as an innovative solution, as it will facilitate the selection of the most appropriate model by integrating algorithms capable of comparing locally collected data with data derived from climate modeling.
SIAC: Freshman Support System with Integration to MICA Chatbot (Interactive Module of Academic Contents)
The freshman’s first contact with the university presents challenges that can impact their academic performance and even lead to dropout. In light of this, this study proposes the development of a web-based support platform for freshmen, integrated with a chatbot, to assist incoming students with their questions about the course and the university. The platform will centralize essential information, guide students on course content, and provide learning resources throughout the program. The chatbot is a key tool for answering questions and will be fed with information from course coordinators. It is expected that this approach will contribute to the adaptation of freshmen, reducing initial difficulties and strengthening the bond between students and the institution, promoting greater academic success and lower dropout rates.
Decentralized Governance in Social Innovation Ecosystems for Greenhouse Gas Emissions Mitigation
Global warming is a humanity’s greatest challenge, and computer science plays a central role in the scalability of social impact solutions. Decentralized autonomous organizations (DAO) refers to an emerging topic that has been gaining prominence due to the growth of collaborative initiatives. This work proposes the investigation of decentralized governance in social innovation ecosystems, with the aim of accelerating the mitigation of greenhouse gas emissions through social participation. Potential models as resulting artifacts can establish guidelines and express relationships between stakeholders to orchestrate different interactions through DAO.
Facial Recognition Systems for Diversity in the Brazilian Context
Facial recognition systems are widely adopted, impacting security, transportation, and services. In Brazil, there are challenges such as algorithmic bias and lack of regulation. This research adopts an ad hoc approach to analyze the literature on facial recognition and diversity, allowing for a flexible and exploratory investigation of the topic. The results highlight gaps in regulation and data representativeness, suggesting as future work a Systematic Literature Review with thematic analysis to deepen the understanding of the social and ethical impacts of this technology. This research is an instance of the emerging theme: Diversity, equity, and inclusion.
Generative AI’s Disruption on Intellectual Property Landscape in Brazil: A Sociotechnical and McLuhan tetrad Analysis
Generative AI (GenAI) introduces transformative challenges and opportunities to Intellectual Property (IP) processes in countries like Brazil, where existing laws, such as Copyright and IP Laws, do not explicitly account for AI-generated nuances. This paper explores the emerging idea behind GenAI’s impact and disruptive potential on the current Brazilian IP landscape. Using a qualitative approach, we apply McLuhan’s tetrad analysis, informed by Sociotechnical Theory, to identify potential enhancements, obsolescence, retrievals, and reversals that GenAI can bring to IP management in Brazil. Our contributions include advancing the understanding of the influence of GenAI on IP and offering preliminary insights for stakeholders to address emerging challenges and optimize opportunities in Brazil’s evolving IP scenario.
Integration, Development and Education of Artificial Intelligence in Public Safety (IDEIA-SP)
This paper aims to present an integrated model for the training and implementation of Artificial Intelligence (AI) in Public Safety. The initiative, called IDEIA-SP (Integration, Development and Teaching of Artificial Intelligence in Public Safety), encompasses three pillars: integration of AI initiatives through a standardized infrastructure; development of systems, partnerships and joint scientific production; and teaching of practical knowledge about AI. IDEIA-SP expects to contribute to improving the analysis of operational and administrative data, reducing the time to resolve incidents and optimizing processes.
NN-DataHub: Database for Neurological Monitoring and Multimodal Correlation in Neonatology
This study proposes the development of a structured database for the storage and analysis of biomedical data from newborns in neonatal intensive therapy units, with an emphasis on neurophysiological signals such as electroencephalography (EEG) and near-infrared spectroscopy (NIRS). Alongside EEG/NIRS, the database will be designed to integrate other physiological parameters such as heart and respiratory rates, peripheral oximetry, motor behavior and reflexes, in addition to clinical/laboratory data, allowing for the correlation between different variables and the early identification of neonatal neurological injuries. The proposed system is intended for clinical use and could be an important tool in research, promoting a more integrated and predictive approach to neonatal monitoring.
Data-Driven Public Policy: The Case of the National Irrigation Information System
This paper discusses the creation of the National Irrigation Information System (SisNIR), a result of a partnership between the Ministry of Integration and Regional Development and the Federal University of Lavras, with the aim of supporting the planning and actions of the National Irrigation Policy. The method consisted of carrying out innovation journeys, data structuring, design and development of software components for SisNIR. Among the main results, we highlight the modeling of a new indicator for analyzing irrigation infrastructure in Brazil, following a multicriteria analysis strategy.
Proposed Gamified Intervention Framework to Combat Procrastination
Procrastination is a persistent and harmful behavior that negatively impacts various aspects of personal, academic, and professional life. Due to its complexity, overcoming this habit presents a significant challenge. Digital technologies, especially persuasive information systems and applications incorporating gamification elements, have emerged as promising alternatives to help combat procrastination. This research proposes a framework for developing gamified information systems aimed at fostering self-regulation and increasing productivity. The objective is to provide a support structure for developers, facilitating the creation of effective solutions tailored to users’ needs and expectations. This framework is expected to contribute to the dissemination of more accessible and engaging technological interventions, promoting sustainable behavioral change.
Structuring software documentation in a low-code development team
This paper presents the emerging results of a study focused on structuring software documentation in low-code development teams, using Microsoft Power Platform as a case study. The research addresses the problem of the lack of structured documentation processes in low-code development environments, highlighting the negative impacts on collaboration, maintenance, and scalability of solutions. The work proposes a centralized and automated approach to documentation, using tools such as SharePoint, Power Apps, and Power Automate. The results demonstrate significant improvements in efficiency, traceability, and integration of new team members, aligning with contemporary trends in automated and collaborative documentation. The study contributes to the area of Process Management and Organizational Information Systems, offering a replicable model for other low-code platforms.
A systematic mapping on interorganizational management and collaboration in the IT context
Context: We already know that organizations establish relationships and collaborate with each other in different ways to be competitive. However, the last few years have been quite challenging due to the period of confinement during the COVID-19 pandemic. Problem: The study aims to identify what has been discussed and developed in the context of interorganizational relationship management in the last few years. Motivation: This research aims to provide an overview of the research projects carried out and what work still needs to be developed in the area. Solution: A systematic mapping was carried out to revisit the area of interorganizational relationship management and how such organizations collaborate in the context of ICT based on the re-execution of the systematic mapping carried out in previous works. Results: During data collection, 132 results were initially found, of which 14 publications were used as the basis for the conclusions obtained.
hansen.app: Simplified Neurological Assessment Form Application for Data Collection to Support Hansen’s disease Treatment
This study addresses the challenge of improving Hansen’s disease understating and monitoring in Brazil’s public healthcare system (SUS) through the development of a digital record system rooted in Soft Systems Methodology (SSM). The proposed solution, the hansen.app, integrates participatory cycles with real stakeholders to adapt and digitize the Simplified Neurological Assessment (from Portuguese ANS) form, ensuring data fidelity and enhanced usability for healthcare professionals and patients. Hence, data modeling and the prototype aim to reduce the complexity of Hansen’s disease care and monitoring by delivering more essential data to improve the knowledge capacity of the healthcare system.
Analysis of Generative AI Use in Creating Gamified Narratives for Software Engineering Education
This study investigates the use of generative Artificial Intelligence (AI) to support Software Engineering teachers to instantiate narrative gamification in their lesson plans. Adopting an action research approach with qualitative and quantitative methods, the research explores the potential of ChatGPT to personalize teaching and optimize the planning of gamified activities. The results show that AI contributes significantly to the creation of engaging narratives, reducing teaching time and effort, but still requires human intervention for pedagogical adjustments.
Proposing a Tool to Monitor Smart Contract Execution in Integration Processes
Smart cities take advantage of digital services to enhance citizens’ experiences. Integration processes facilitate interactions between these services, providing or improving functionalities. The integration can operate under specific restrictions, which can be represented through smart contracts deployed in a blockchain. Monitoring systems track interactions between integration processes and digital services by recording events from communication ports. In this paper, we argue that current monitoring tools lack the ability to observe these ports or invoke smart contracts. We propose a monitoring system to track integration processes, capture port-reported events, and invoke smart contracts on a blockchain platform.
Strategic integration of generative ai in design artifact ideation: proposal of a structured framework
This paper proposes a structured framework for strategically integrating Generative Artificial Intelligence (GAI) into the design ideation process, aiming at optimizing artifact creation while preserving designers' creative autonomy. Grounded in the Design Science Research (DSR) methodology, the framework was validated through a controlled quasi-experiment. Quantitative results showed a significant 40% reduction in ideation time and a substantial 60% increase in creative variability. Qualitative analysis confirmed participants' positive acceptance, emphasizing the framework’s flexibility and ease of customization. Nevertheless, challenges emerged regarding the initial training required for users, as well as concerns about originality and ethical implications of AI-generated content.
Applying the ITIL 4 Infrastructure Management practice in a customized model for Municipal Public Administration
This study proposes a customized process model based on the Infrastructure and Platform Management technical practice of ITIL 4 for implementation in a medium-sized municipality. To develop this model, the Design Science Research (DSR) approach was adopted, following its methodological steps. The model's demonstration was carried out through a quantitative survey with experienced ICT professionals. Before its future implementation, the current maturity level of the studied municipality was assessed using a diagnostic questionnaire. The model aims to improve the quality and efficiency of ICT infrastructure management, promoting continuous improvements and enhancing the delivery of public value.
Identifying Jurisprudential Divergences with Artificial Intelligence Techniques to Support Judicial Information Systems
This work explores the application of Natural Language Processing and Machine Learning techniques to detect jurisprudential divergences, contributing to greater legal certainty. The methodology encompasses data preprocessing of judicial summaries, the use of the Word2Vec neural network technique for word embedding, and the clustering of 3,163 court rulings using K-Means to identify semantic similarities and divergences. By presenting examples of discrepant decisions, the study demonstrates the potential of the approach to promote jurisprudential uniformity. The findings support the development of information systems that assist in legal interpretation and enhance the predictability of decisions, ultimately fostering a fairer society with greater equity, inclusion, and diversity.
JASI: An Educational Guide to Support Female Students’ Journey in an Information Systems Program
This article presents the development and evaluation process of the JASI Guide (acronym of ”Jornada da Aluna de Sistemas de Informação”), a web-based educational tool to support female students in the Information Systems course. The guide provides information about the course curriculum, research laboratories, and study materials, organized in a more attractive format. The research followed a quali-quanti approach, in which semi-structured interviews were conducted with ten female students to identify the academic challenges they faced. The usability evaluation was then carried out using the System Usability Scale (SUS) questionnaire, which allowed us to analyze the tool’s potential to promote inclusion and diversity in the academic environment.
Operationalizing sustainable transparency in software ecosystems portals
Transparency is a key element in software ecosystems (SECO) and promotes trust, collaboration, and accountability. However, current transparency measures focus mainly on technical aspects, such as code openness or Application Programming Interface (API) accessibility, while focusing less on social, organizational, and economic characteristics. This paper introduces a Key Performance Indicator (KPI) framework to operationalize sustainable transparency in SECO portals. Our mixed method begins with a longitudinal review of previous research and proceeds to define KPI aligned with measurable goals. Expert evaluations will be conducted, through structured interviews, to assess the feasibility of the proposed KPI. In addition, a feasibility study in a representative SECO setting will explore how this framework can strengthen existing transparency efforts and inform continuous improvements in ecosystem management. The goal of this research is to contribute to the field of information systems by providing actionable data-driven insights that can enable SECO management teams to monitor and improve transparency across multiple characteristics and thus support the long-term sustainability of digital platforms.
Guidelines for Systematic Fault Tolerance Requirements Extraction in Systems-of-Systems from Business Process-of-Processes
This paper presents a set of guidelines for the systematic extraction and specification of fault tolerance requirements in Systems-of-Systems (SoS) based on Processes-of-Business Processes (PoP) models in BPMN. The extracted requirements aim to reach reliability during the SoS interoperability, which automate the corresponding PoP, and the alignment between the technical and business levels. The guidelines were evaluated through a study case. The results indicate that the guidelines support the systematic extraction of fault tolerance requirements from essential information of the business level.
A Delphi Study on the Adaptation of SCRUM Practices to Remote Work
This study explores how Scrum practices were adjusted for remote and hybrid work during and after the COVID-19 pandemic, using a Delphi study with Scrum Masters to gather expert insights. Preliminary key findings highlight communication as the primary challenge, leading to adjustments in meeting structures, information-sharing practices, and collaboration tools. Teams restructured ceremonies, introduced new meetings, and implemented persistent information-sharing mechanisms to improve their work.
Intelligent Platform for Industry-Academia Collaboration in Research Project and Researcher Prospecting
One of the main factors influencing the adoption of new technologies in industry and government is the collaboration between Industry, Academia, and Government. Through Research and Development, it is possible to drive digital transformations and create new applied technologies. This paper proposes conducting Systematic Literature Reviews (SLR) and developing an intelligent platform that uses Natural Language Processing techniques and Large Language Models to enhance these collaborations. So far, we have completed two comprehensive SLR, developed two versions of a project prototype, and the goal is to enable information sharing and interconnection with institutions and researchers.
Automating Mobile App Testing with Reinforcement Learning Using User Story Requirements
Information systems are mainly accessed via mobile applications. Ensuring their quality is crucial, which is why software testing is essential. However, creating and executing manual tests requires a lot of effort. This study investigates the automation of test generation for Android applications, combining the reinforcement learning DRL-MOBTest tool with language models to interpret and structure requirements extracted from user stories written in Gherkin. In experiments conducted with 10 applications, the results indicated an average requirements coverage of 81.41%. Despite the good results, the solution has limitations for scenarios with more sequential interactions. These findings may contribute to research in the application validation using artificial intelligence.
The Role of Artificial Intelligence in Promoting Peace, Justice, and Strong Institutions in Brazil: Opportunities, Challenges, and Risks
Artificial Intelligence (AI) plays a critical role in advancing Social Development, offering transformative opportunities alongside significant risks. This study examines the dual role of AI in supporting SDG 16—Peace, Justice, and Strong Institutions—with a particular focus on Brazil’s unique socio-political and technological landscape. Through a comprehensive literature review, the research aims to highlight how AI applications can improve efficiency, transparency, and crime prevention, while raising challenges such as bias, privacy erosion, and inequality.
A Conceptual Framework to Support Organizational Knowledge Transfer Evaluation
Evaluating Knowledge Transfer (KT) is critical for driving organizational learning, optimizing operational efficiency, and fostering innovation. However, knowledge on this topic remains unsystematized, existing solutions lack empirical validation, and standardized approaches are not widely adopted. This paper proposes an approach to support KT evaluation. To this end, a rapid review was conducted, and the Design Science Research Methodology was employed to model the approach. Given the impact of KT on organizational information flows and decision support systems, we present an overview of the systematization of knowledge on KT evaluation and the structure of its key processes and elements.
Implementation and Validation of a Prototype System for Hate Speech Detection Using Artificial Intelligence
This study proposes a prototype system leveraging the BERTimbau model to detect hate speech in chat conversations, specifically tailored to the linguistic and contextual features of Brazilian Portuguese. In this way, the research explores how automated moderation systems can be integrated into digital environments to enhance social interactions. A prototype system was developed and evaluated using a descriptive, quantitative approach, achieving satisfactory performance metrics in identifying offensive language and demonstrating efficient response times. The study highlights the potential of NLP models to improve content moderation in Portuguese, offering valuable insights for academia and the private sector to strengthen digital safety strategies.
Automatic Information Extraction from Neurological Assessment Records for Leprosy Cases Using AI
Leprosy is a chronic infectious disease that continues to be present in more than 120 countries. In 2023, over 20,000 new cases were reported in Brazil, making it the second most endemic country in the world. The development of new information systems that utilize available clinical records is essential to support decision-making during the treatment of this highly stigmatizing disease. In this study, we present preliminary results demonstrating the feasibility of using YOLO for the automatic recognition of non-textual data from neurological assessments of patients undergoing treatment. Our approach achieved an accuracy of 97.5% in recognizing sensory assessment records used in the Brazilian healthcare system.
Technical Debt Management Practices in Agile Development: results of a scientific initiation project
The iterative and incremental nature of agile methods can create an environment ripe for the accumulation of Technical Debt (TD). Faced with these challenges, the importance of academic research in solving these problems stands, driving improvements in software quality. This study to improve TD management in agile methods. For purpose, a briefing of evidence obtained through a survey developed in a scientific initiation project was constructed. Among the results, we identified schedule pressure as a cause for the occurrence of TD.
Strengthening Privacy in Virtual Private Network Authentication: A Research Proposal Exploring Blockchain and Zero-Knowledge Proofs
With the advancement of Information Systems, data privacy has become a central concern. Virtual Private Networks (VPNs), although widely used, present vulnerabilities related to traditional authentication methods (username and password) and the use of traceable payment methods, such as credit cards. This ongoing research proposes an architecture for anonymous authentication in VPNs, based on blockchain and Zero-Knowledge Proofs (ZKP), which allows user identity to be validated without exposing it, while cryptocurrency payments eliminate identifiable data. The solution aims to offer greater security, privacy, and anonymity, being relevant, for example, for citizens in contexts of surveillance and censorship.
A Model-Based Approach Using SSN for Health and Quality Analysis in Software Ecosystems
This research proposes an approach based on Software Supply Network (SSN) models to analyze the health and quality of Software Ecosystems (SECO). The proposal aims to address gaps in the literature by providing a structured process, from identifying the need for analysis to compiling results. The approach includes SSN modeling, defining quality indicators, and applying statistical and qualitative analyses. As an emerging study, the expected outcomes contribute to developing effective methods for evaluating and improving the sustainability and resilience of SECO.
Playing for Inclusion: Gamification as a Tool to Teach SDGs to Autistic Children
The Autism Spectrum Disorder is characterized by persistent deficits in social communication and interaction, which present significant challenges to the teaching and learning process. In this context, Active Learning Methodologies, such as gamification, emerge as effective alternatives to promote inclusion and skill development. This study explores gamification through an educational game designed to teach the Sustainable Development Goals to autistic children. Applying the Design Thinking methodology and the Double Diamond model, the game is intended for autistic children aged 6 to 10. It is expected that the interactive approach will increase engagement and facilitate children’s learning.
DEAF: A Tool for Data Query and Analysis in SAMU
This paper presents the Data Extraction and Analysis Framework (DEAF), an API developed to automate data extraction and analysis in the Mobile Emergency Care Service (SAMU). The e-SUS SAMU system has limitations such as manual queries and inconsistencies in reports, which hinder decision-making. DEAF addresses these issues through dynamic queries, automated report generation, and enhanced security. This study describes the system’s operation, implementation, and benefits, highlighting its impact on operational efficiency and data management transparency in the SAMU service in the city of Marabá, Brazil.
A Machine Learning Approach to Evaluate Privacy Policies in an Information Systems Development Context with Privacy by Design
A adoção da ”Privacidade por Design”tornou-se essencial no desenvolvimento de sistemas de informação devido à LGPD, exigindo a incorporação da privacidade desde as fases iniciais do desenvolvimento. No entanto, as políticas de privacidade costumam ser extensas e complexas, dificultando sua compreensão e aplicação prática, o que pode comprometer a conformidade legal. Para solucionar esse problema, este estudo propõe uma abordagem baseada em aprendizado de máquina para avaliar e melhorar a transparência dessas políticas. A metodologia incluiu a análise de 23 políticas e a aplicação de diferentes algoritmos para avaliar sua clareza e adequação legal. O melhor algoritmo apresentou 81% de eficácia, enquanto a ferramenta desenvolvida atingiu 72,7% na avaliação das políticas em conformidade com ”Privacidade por Design”. Os resultados mostram que a ferramenta pode auxiliar desenvolvedores na implementação de requisitos de privacidade desde o início dos projetos. Assim, este estudo reforça o uso de aprendizado de máquina para integrar privacidade ao ciclo de vida do software.
Block Learning to assist the Daily Routine of Autistic Children
Autism, or Autism Spectrum Disorder (ASD), is a neurodevelopmental disorder that impairs the ability to express many emotions, thoughts and feelings. This research aims to develop an interactive web platform "Magic Sequence" for children with ASD aged 2 to 4 years, to support them in learning sequences and daily routines from visual and interactive resources. Based on a qualitative analysis of the data and observation of the educational context, it is expected that this research will help educators and families to use accessible and adapted technologies.
Integration and Visualization of Maternal and Child Health Data: A Computational Architecture for Analysis and Decision Support
The increasing volume of data generated by health information systems demands new approaches for analysis and visualization, particularly in the context of public health. This paper proposes the development of a computational architecture to integrate data from three major Brazilian Health Information Systems (HIS): SIM (Sistema de Informação sobre Mortalidade), SINASC (Sistema de Informação sobre Nascidos Vivos), and SIH (Sistema de Informação Hospitalar). The primary objective is to facilitate integrated data analysis and visualization, enabling the extraction of insights to support public policy formulation. Partial results demonstrate the effectiveness of the proposal through the extraction and integration of datasets into a unified database, which allows for temporal, regional, and socio-contextual analyses.
FakeCheck: Exploring the Communicability of a Fake News Detection Tool
The spread of fake news impacts society, and detection tools have been developed to mitigate its effects. FakeCheck is one such solution, but its interface must be clear and intuitive to ensure good usability and greater effectiveness in combating misinformation. This study evaluates the communicability of FakeCheck’s interface from a Human-Computer Interaction (HCI) perspective, from Semiotic Engineering. The results reveal communication barriers in the interface, affecting the user-system interaction, such as unclear instructions and verification results. Based on these findings, improvements are suggested to make the interface more accessible and reliable.