CLEI Electronic Journal

Preface to the CLEI 2025 Special Issue, Volume II

Sergio Ochoa — 2026-08-06

The present issue of CLEI Electronic Journal (CLEIej) is devoted to extended and revised papers of selected works presented at the 51^st Latin American Informatics Conference (CLEI 2025), held in Valparaíso, Chile, from October 27^th to 31^st, 2025. This is the second volume of selected articles from the conference.

The CLEI 2025 conference included five major tracks on different computer science and informatics areas, particularly: Computer Technology, Intelligent Systems, Software Systems, Systems In Practice, and Computer Science Education. A total of 275 submissions were received in these tracks; 91 were accepted as full papers and 23 as work-in-progress. It resulted in an acceptance rate of 33,1% for full papers.

Based on the recommendations of the track chairs, we invited the authors of the 20 papers with the highest score to extend their work and submit it to this special issue. The authors of 14 papers accepted the invitation, and submitted an extended version of the previous article. These articles were reviewed by external researchers and revised by the authors accordingly. This process resulted in five papers being accepted and already published in the first volume of this special issue, and six papers accepted for publication in this second volume. Next we introduce the articles included in this volume of the special issue:

“Practical Machine Learning for Self-Tuning Buffer Pools in DBMS Mainframe Systems”. This work focuses on the automated optimization of buffer pools in database management systems running on mainframes, a critical area for performance in high-demand enterprise environments. Its main contribution is an empirical validation in a real production setting, applying a methodology that integrates exploratory factor analysis, LASSO regression, and Bayesian optimization with Gaussian processes to tune buffer pool configurations based on actual workloads. Tested in a large-scale financial system, the approach achieved substantial performance improvements, including up to a 95% reduction in maximum synchronous I/O wait time, demonstrating that machine learning techniques are both practical and effective in mission-critical mainframe environments.

“Enhancing an Arbovirus Control System with Georeferencing and Geolocation Resources”. This paper addresses the enhancement of arbovirus control systems, through the integration of advanced georeferencing and geolocation technologies. Focusing on dengue and other mosquito-borne diseases in Brazil, the study introduces innovative features that shift operational focus from postal addresses of individual properties to block-based polygons of areas, significantly improving spatial accuracy and data reliability. The system, tailored specifically for field agents and health coordinators, facilitates precise visit validation, allows building dynamic heat maps, and optimized route plans, ultimately enabling more targeted and efficient vector control efforts. The main contribution of this work lies in developing a modular, scalable geospatial architecture embedded within existing health management platforms, which has demonstrated substantial improvements in operational effectiveness and strategic decision-making in disease surveillance.

“Beyond PageRank in GraphHD: Centrality Metrics and Efficient Hyperdimensional Encodings”: This article addresses graph classification within the field of Hyperdimensional Computing (HDC). It proposes efficient hypervector encoding methods that extend the GraphHD framework, which is the basis of this proposal. The authors systematically evaluate alternative graph centrality indicators—e.g., degree, closeness, betweenness, Katz, and eigenvector—in place of the standard PageRank to assign node hypervectors. Additionally, they introduce two novel encoding variants: GraphHD-Level, which preserves quantitative centrality information, and GraphHD-Order, a simplified method that omits edge encoding altogether. Experimental results on six benchmark datasets demonstrate that replacing PageRank with alternative centralities maintains comparable classification performance, while significantly reducing encoding time. Notably, GraphHD-Order achieves similar accuracy to the baseline, but offers consistent speedups (especially on large graphs), making it an attractive lightweight alternative for resource-constrained scenarios. This study highlights the potential of HDC for efficient and robust graph representation and classification tasks.

“IT Governance and Corporate Sustainability: Decision Frameworks for Digital Transformation Aligned with GRI Standards”: This study explores the role of IT governance and management frameworks—such as COBIT, ITIL, CMMI, and Six Sigma—in facilitating organizational sustainability within the context of digital transformation, aligning with Global Reporting Initiative standards. Through a systematic literature review that analyzed 61 primary studies, this work identifies how these frameworks support economic, environmental, and social sustainability indicators, by improving operational efficiency, waste reduction, and quality enhancement. The research highlights that IT frameworks not only optimize internal processes, but also enable the conversion of technical data into measurable sustainability metrics, thereby informing strategic decision-making and promoting long-term sustainable value creation. This work contributes with a structured decision-making guide for organizations seeking to integrate digital transformation initiatives with sustainability objectives.

“Early Computing Education in Brazilian Public Schools: Insights from Teachers”: This paper addresses the Computing Education field, with a particular focus on K–12 computer science education, teacher professional development, and gender inclusion in STEM. It presents a mixed-methods study that investigates teachers’ perceptions of the long-running Meninas.comp outreach initiative, which introduces programming, robotics, and computational thinking into Brazilian public schools. Unlike most previous studies that primarily assess students’ experiences, this work places teachers at the center of the evaluation, providing new insights into their professional development, pedagogical practices, and perceived impacts on students, schools, and local communities. The study demonstrates that participation in the project strengthens teachers’ technical and pedagogical skills, increases student engagement—particularly among girls—and fosters broader institutional and social benefits, highlighting the crucial role of teachers as agents of sustainable and inclusive computing education.

“Agent-Based Modeling and Simulation - Feature Spaces, Macroscopic Statistical Analysis, and Specification Fidelity”. This work focuses on evaluating Agent-Based Modeling (ABM) frameworks, by proposing the Feature Space Maturity Model to systematically assess how well frameworks meet modeling task requirements. It also introduces a macroscopic statistical protocol to validate implementation fidelity across multiple frameworks. Testing six frameworks on a standardized experiment, the study finds that five of them produce statistically equivalent results, highlighting the value of statistical validation for ensuring reliable and reproducible ABM simulations.

After concluding the process of preparing, processing and publishing these two-volume special issue, we would like to express our deep appreciation to all the CLEI 2025 Track Chairs for their collaboration throughout the paper review process, and their vital role in evaluating and selecting the papers invited to this special issue.

We also extend our sincere thanks to the reviewers, who selflessly dedicated their time and expertise to assess the content and quality of the papers featured in this issue.

Finally, we are grateful to the authors for accepting the invitation to extend their conference papers, and incorporate the reviewers' feedback to create these high-quality extended versions of the manuscripts.

We hope you find this special issue as insightful and enjoyable as we do, as it helps to disseminate significant research from the Latin American Computer Science community.

Ignacio Araya, Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso (Chile)

Carlos Luna, Instituto de Computación, Universidad de la República (Uruguay)

Sergio Ochoa, Departamento de Ciencias de la Computación, Universidad de Chile (Chile)

CLEIej Guest Editors for CLEI 2025 Special Issue - Volume II

Practical Machine Learning for Self-Tuning Buffer Pools in DBMS Mainframe Systems

Eduardo Pingarilho Mendizabal — 2026-08-06

This paper investigates the practical applicability of Machine Learning (ML) techniques for buffer pool optimization in Database Management Systems (DBMSs), a critical component traditionally tuned manually in mission-critical environments. While prior work proposes ML-based approaches, most evaluations are limited to simulated settings, leaving their feasibility in production systems—particularly on mainframes—largely unexplored. To address this gap, we present an empirical, production-level validation of an automated, data-driven optimization methodology applied to a relational DBMS running on a mainframe. Our approach integrates Bayesian Optimization based on Gaussian Process Regression (BO-GPR) with a three-phase pipeline: Exploratory Factor Analysis with clustering for metric reduction, LASSO regression for parameter selection, and BO-GPR for fine-grained configuration tuning. We validate the methodology using real workloads from a large-scale financial system, achieving substantial performance gains, including up to a 95\% reduction in maximum synchronous I/O wait time. These results provide reproducible evidence that ML-based optimization is both effective and operationally viable in mainframe-based DBMSs.

Enhancing an Arbovirus Control System with Georeferencing and Geolocation Resources

João S. Castanho — 2026-08-06

Diseases transmitted by Aedes aegypti, such as dengue, zika, and chikungunya, are a significant challenge for public health in Brazil. Geolocation and georeferencing are relevant resources to enhance information systems that support the surveillance and control of such diseases. Despite its importance, many current methods of surveillance and control still rely on manual, paper-based processes that are slow and prone to spatial inaccuracies. This work presents the integration of advanced georeferencing resources into the ``Controle Vetorial Paraná'' system, a technological solution designed to modernize entomological surveillance for the state of Paraná, Brazil. The proposed approach shifts the operational focus from individual property addresses to block-based polygons, overcoming the precision limitations of public geocoding services. Key features include the implementation of the Ray Casting algorithm for the mandatory geographic validation of field visits and the generation of dynamic heat maps for strategic decision-making. The solution was validated through user-centered design stages with health coordinators and endemic control agents. Results demonstrate that the transition to a polygon-based model, combined with a 35-meter GPS tolerance zone, significantly enhances the reliability of field data and streamlines the planning of blocking routes, providing a more precise and efficient tool for public health management. The experience reported in this work is worthwhile for a wide range of studies on applying georeferencing in information systems.

Beyond PageRank in GraphHD: Centrality Metrics and Efficient Hyperdimensional Encodings

Ignacio Sica — 2026-08-06

Graph classification plays a central role in many scientific disciplines. While classical kernel-based methods and graph neural networks achieve strong predictive performance, they often require substantial computational resources. Hyperdimensional Computing (HDC) has recently emerged as an efficient and noise-resilient alternative, providing lightweight models that are attractive for resource-constrained settings. Within this context, GraphHD is a representative HDC-based approach for graph classification; however, its encoding process can become costly on large graphs and its standard configuration relies on a single centrality choice (PageRank) for node-to-hypervector assignment.

In this work, we go beyond PageRank in GraphHD by systematically evaluating alternative centrality measures (degree, closeness, betweenness, Katz, and eigenvector) and by introducing two new encoding variants. GraphHD-Level preserves quantitative structural information by mapping centrality values to level-hypervectors, whereas GraphHD-Order simplifies the algorithm by eliminating edge encoding and aggregating node hypervectors directly. Experiments on six widely used benchmarks from cheminformatics and bioinformatics (MUTAG, ENZYMES, PROTEINS, DD, NCI1, and PTC\_FM) show that replacing PageRank with alternative centralities yields similar F1-scores while offering notable runtime savings, and that GraphHD-Order remains competitive with the original GraphHD baseline while providing consistent speedups in encoding time.

IT Governance and Corporate Sustainability: Decision Frameworks for Digital Transformation Aligned with GRI Standards

Suyana Arcos Villagómez — 2026-08-06

Digital transformation represents a fundamental opportunity for organizations to incorporate sustainability criteria into their strategic decisions. In this context, IT governance and management frameworks—such as COBIT, ITIL, CMMI, and Six Sigma—provide structures to guide technology projects aligned with economic, environmental, and social objectives. This article presents a Systematic Literature Review (SLR) that analyzed 61 primary studies selected from 126 initial records. The research work was based on the Kitchenham methodology and the PRISMA 2020 statement; to identify how these technological governance and management frameworks act as facilitators in the adoption of sustainability indicators under GRI Standards. The results reveal a clear association between cost reduction and increased productivity within the economic dimension; waste reduction reflects progress in the environmental dimension; and the improvement of quality and value delivered to stakeholders aligns with the social dimension. In conclusion, sustainability is not an abstract concept, but a measurable result of mature process management; which allows organizations to make informed strategic decisions and communicate their performance in a comprehensive manner.

Early Computing Education in Brazilian Public Schools: Insights from Teachers

Aline De Galés Silva — 2026-08-06

This article presents a mixed method study conducted with elementary and secondary school teachers involved in a project that promotes interest in computing through computer science, robotics, and programming in Brazilian public schools. The study examines teachers' perceptions about the impact of the project on their professional development, student learning and participation, and surrounding school and community contexts. The results highlight the importance of early exposure to computing in fostering inclusion and social transformation. Teachers report positive outcomes, including the development of technical skills, increased motivation to teach, and significant improvements in the learning and participation of girls. Broader impacts at the school and community levels, such as increased parental participation, further underscore the role of teachers in initiatives aimed at broadening participation in computing.

Agent-Based Modeling and Simulation - Feature Spaces, Macroscopic Statistical Analysis, and Specification Fidelity

Luis Carlos Lara-López — 2026-08-06

Agent-Based Models (ABMs) are widely used to simulate complex systems through emergent behavior. Agent Based Modeling practice is constrained by the lack of systematic methods to evaluate and select frameworks for a given problem, and by the absence of protocols to study differences across computational implementations. We propose a Feature Space Maturity Model (FSMM) to evaluate how well a framework matches the requirements of a modeling task. We present results of four canonical ABM experiments to demonstrate the FSMM applied to five well-known frameworks. Our results suggest that combining the FSMM with the proposed protocol provides valuable information for both framework builders and modelers facing complex technical and research choices.

Ensuring ABM reliability requires specification fidelity -- meaning both the conceptual correctness of the model and its faithful implementation. We study implementation fidelity by analyzing statistical similarity across multiple frameworks. We apply a macroscopic statistical approach to evaluate whether different ABM frameworks yield functionally equivalent results when executing the same specification. We designed a Pareto-based experiment and implemented it in six ABM frameworks. We defined three macroscopic observables and analyzed 10,000 simulation runs per framework using coefficient of variation, Fréchet distance, ANOVA, and F-tests. Five of the six frameworks show statistically indistinguishable behavior, while one exhibits consistent deviations. The results highlight the value of statistical validation to identify implementation inconsistencies and optimize resource allocation in large-scale simulations.