TCC - Bacharelado em Sistemas da Informação (Sede)
URI permanente para esta coleçãohttps://arandu.ufrpe.br/handle/123456789/427
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13 resultados
Resultados da Pesquisa
Item Como o uso de Play Feature Delivery no Android pode ajudar na sustentabilidade digital(2023-09) Claudino, Yasmmin Maria Monteiro; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/0549149216731460With the increasing access to the internet via mobile devices among the less privileged classes in Brazil, digital sustainability becomes of growing importance. The aim of this study is to assess the impact of the Play Feature Delivery technology on mobile data consumption on Android devices. A parametric t-test was applied to evaluate significant differences between the averages of data amounts spent in Megabytes when downloading two applications. The result corresponded to a t-value of approximately 65.55 and the rejection of the null hypothesis. This finding not only underscores the technical importance for Android developers but also highlights its relevance in showing an improvement in mobile data usage, especially in an era where the democratization of access to information is vital. The research reinforces the idea that society should adapt to digital resources, optimizing data usage. To reach these conclusions, two mobile applications focused on first aid guidelines were developed and analyzed. The main advantage observed was the reduction in mobile data consumption, validating the efficacy of Play Feature Delivery compared to conventional applications.Item Construção de pipelines de dados sobre obras públicas em Pernambuco: abordagem prática com o Apache Airflow(2023-09-21) Silva, Henrique César José da; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584This study presents a practical approach to building data pipelines focused on collecting, transforming, and storing information related to public works in the state of Pernambuco. The central objective is to develop efficient and automated workflows for extracting data from public transparency portals and subsequently consolidating this information. Based on Data Engineering technologies, the Apache Airflow framework was chosen to orchestrate the processes, enabling the scheduling and monitoring of these workflows.Item An implementation of a mathematical-computational method for the detection and treatment of financial outliers in higher education(2023-09-06) Freitas, Nathan Cavalcante; Gouveia, Roberta Macêdo Marques; http://lattes.cnpq.br/2024317361355224; http://lattes.cnpq.br/1613649528791400The Higher Education Census occurs annually, collecting data from public and private Higher Educational Institutions (HEI) in Brazil. Different factors can lead to anomalies or outliers in some of these collected data. This work proposes a mathematical-computational method to detect and treat atypical HEI’s financial values. Both univariate and bivariate analysis to that end. We analyzed the expenses and incomes of HEI in the census from 2016 to 2019. This analysis revealed that 204 out of 2,224 HEI, approximately 10%, reported some atypical data.Item Geração de indicadores de desempenho de negócio utilizando o Data Warehouse de uma grande rede de varejo(2023-05-04) Tavares, Luís Felipe do Carmo Costa; Gouveia, Roberta Macêdo Marques; http://lattes.cnpq.br/2024317361355224The use of Key Performance Indicators (KPIs) is essential for companies as it allows them to evaluate the performance and outcome of processes, facilitate decision-making, identify patterns of sector performance, reduce unnecessary costs, and increase operational efficiency. However, it is important to take into account that the calculation of KPIs becomes more complex due to the company's growth and the arrival of new demands. This project was implemented in a large retail network that needed to reformulate the way it calculated its KPIs due to the emergence of new branches and an increase in employees. To improve performance, the centralization of calculations was performed through a Data Warehouse model (with Data Lakes), where calculations with changes are being performed in a single database instead of in each branch's individual database and later centralized. During the project's implementation, several challenges were faced, such as validating the result of each indicator, creating new features (such as cumulative calculation), and the need to create a specific procedure for each metric and indicator.Item Desenvolvimento de uma plataforma de compartilhamento e análise de Dados com ênfase em LGPD(2023-04-27) Monteiro, Heitor Augusto Gomes; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505Item Desenvolvimento de um plug-in para a replicação de dados entre os sistemas NetBox e ServiceNow CMDB(2023-05-03) Silva Júnior, Manassés Júlio da; Gouveia, Roberta Macêdo Marques; http://lattes.cnpq.br/2024317361355224This work presents a new plugin developed to integrate the open source network configuration software NetBox with the ServiceNow CMDB. This plugin extends the functionality of NetBox, allowing users to send NetBox data to the ServiceNow API. NetBox is an open source web application that helps manage and document computer networks. It acts as a centralized repository for network infrastructure information, including device inventory, IP address management, cable management, and power management. On the other hand, the ServiceNow CMDB is a central repository that contains information about all assets and configuration items in an organization's IT infrastructure. The integration between these platforms is achieved through the creation of plugins that extend the functionality of NetBox, allowing it to work together with the ServiceNow CMDB. The project uses Python as the main language, the Django web framework, and Docker to create the development environment. Overall, this project provides a powerful and flexible tool for network administrators and operators to manage their network infrastructure. The plugin architecture follows the Django MTV (Model-View-Template) architecture, where the Model represents the data and database schema, the View handles requests and responses, and the Template generates the HTML output. The main functionality of the project is the automatic replication of Create, Read, Update and Delete (CRUD) modifications in selected objects from NetBox to the ServiceNow CMDB, done through the ServiceNow API. This automatic replication feature uses Webhooks to monitor object modifications, and the plugin automatically handles the creation and deletion of Webhooks. Other features include manual batch and simulation for replicating data to the CMDB. The visual interface of the plugin is simple and focused on its functionalities.Item Processo de Renovação Generalizado baseado na distribuição Gumbel como modelo de estimativas de ocorrências de ondas de calor para auxiliar no processo de tomada de decisão do cultivo de manga no Sertão de Pernambuco(2023-05-08) Ferraz, Kimbelly Emanuelle Avelino; Cristino, Cláudio Tadeu; http://lattes.cnpq.br/0295290151219369; http://lattes.cnpq.br/2320958356149704Several types of events can harm the planting, harvesting or handling of plants and fruits in agricultural areas, one of them including the event called heat waves, which is characterized as a prolonged and relatively uncommon meteorological phenomenon with extremely high temperatures for the region and persistent for several days or even weeks. Given the importance of agriculture, this work seeks, through the analysis of the maximum temperature data in the Petrolina region, the study of the mango plantation, the Heat Wave event through the 90th percentile, optimization algorithms and the processes of generalized renewal and Gumbel, estimating this event contributing to the farmer’s decision making and optimization of Mango production. The proposed model uses the generalized renewal process based on the Gumbel distribution (GuGRP) to model the time intervals between heat waves, considering that consecutive events are conditionally independent. This model proved to be adherent to model events with a significance level of 0.05 and a P −V alue of 0.28 through the Kolmogorov-Smirnov adherence test on the adequacy data adapted to the GuGRP. The model parameters were estimated by Log-Likelihood using optimization algorithms, also specifically testing the Particle Swarm algorithm.Item Explainable Artificial Intelligence - uma análise dos trade-offs entre desempenho e explicabilidade(2023-08-18) Assis, André Carlos Santos de; Andrade, Ermeson Carneiro de; Silva, Douglas Véras e; http://lattes.cnpq.br/2969243668455081; http://lattes.cnpq.br/2466077615273972; http://lattes.cnpq.br/3963132175829207Explainability is essential for users to efficiently understand, trust, and manage computer systems that use artificial intelligence. Thus, as well as assertiveness, understanding how the decision-making process of the models occurred is fundamental. While there are studies that focus on the explainability of artificial intelligence algorithms, it is important to highlight that, as far as we know, none of them have comprehensively analyzed the trade-offs between performance and explainability. In this sense, this research aims to fill this gap by investigating both transparent algorithms, such as Decision Tree and Logistic Regression, and opaque algorithms, such as Random Forest and Support Vector Machine, in order to evaluate the trade-offs between performance and explainability. The results reveal that opaque algorithms have a low explanability and do not perform well regarding response time due to their complexity, but are more assertive. On the other hand, transparent algorithms have a more effective explainability and better performance regarding response time, but in our experiments, we observed that accuracy obtained was lower than the accuracy of opaque models.Item Uso de machine learning para previsão de valores de apartamentos no município do Recife(2023-09-12) Silva, Thiago César de Miranda; Monteiro, Cleviton Vinicius Fonsêca; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/8285740572952516The COVID-19 pandemic has brought with it a series of economic effects and transformations related to behavior and the way people live, which, in turn, have had repercussions on property prices and real estate demand. In this context, property price forecasting assumes an extremely important role, contributing to more informed decisions, mitigating risks, and promoting greater transparency in the real estate sector. The implementation of automation in price forecasting further enhances this dynamic, significantly improving accuracy, efficiency, and reliability of predictions, while providing adaptability to economic fluctuations with greater agility. Utilizing listings available on OLX, a georeferenced database was created to generate a residential apartment price prediction model in Recife, using machine learning models in AutoML. This tool automates the development of machine learning models, enabling rapid experimentation and a focus on problem-solving. The work indicates that the poor geographical distribution of the data has biased the results of the models. Furthermore, it was concluded that the data found on online buying and selling platforms are insufficient for generating a machine learning model that achieves an acceptable level of accuracy in Recife, mainly because transaction values for the properties are not provided, only the advertised prices. However, this current work provides significant contributions to the advancement of research related to automation in real estate price prediction.Item Recomendação de psicólogos por meio de algoritmos de filtragem colaborativa, conteúdo e híbrida(2023-09-14) Gomes Júnior, Augusto Rosário; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863There is a rising number of people diagnosed with mental health disorders such as depression and anxiety, disorders that have been long neglected by science and society. Even so, more and more advances are being made in the ways of treating these people, such as platforms that offer psychological care remotely. However, choosing a psychologist or therapist is not always an easy task, given the large amount of information involved in the choosing process. Based on that, the goal of this article was to develop a psychologist recommender system based on a hybrid model, which should be able to recommend psychologists with expertise that meet the needs of different types of patients. The model showed promising results, where the similarity between the recommended psychologists was consistent and good results were achieved in the evaluation metrics MAE (<0.5) and RMSE (<0.75). It was also possible to mitigate weaknesses from both content and collaborative recommendations.