Bacharelado em Sistemas de Informação (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/12
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Item Uma abordagem baseada em aprendizado de máquina para dimensionamento de requisitos de software(2016-12-13) Fernandes Neto, Eça da Rocha; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/6325583065151828This work proposes to perform the automatic sizing of software requirements using a machine learning approach. The database used is real and was obtained from a company that works with Scrum-based development process and Planning Poker es- timation. During the studies, data pre-processing, classification and selection of best attributes were used along with the term frequency–inverse document frequency algo- rithm (tf-idf) and principal component analysis (PCA). Machine learning and automatic sorting occurred with the use of Support Vector Machines (SVM) based on available data history. The final tests were performed with and without attribute selection by PCA. It is demonstrated that the assertiveness is greater when the best attributes are selected. The final tool can estimate the size of user stories with a generalization of up to 91 %. The results were considered likely to be used in the production environment without any problems to the development team.Item Análise quantitativa entre métodos de estimativa da evapotranspiração horária integrada e diária no Brasil(2018) Andrade, Danyllo Gomes Figueredo de; Gonçalves, Glauco Estácio; http://lattes.cnpq.br/6157118581200722; http://lattes.cnpq.br/9219132484229871Evapotranspiration is the fusion of two natural processes of soil water loss, which are:evaporation and transpiration. The evaporation consists of a change of state of thewater that is contained in the soil, passing from the liquid to the gaseous state. In turn,the transpiration of plants occurs when the vegetable performs the nutrition process.Quantitative information from such processes provides a more accurate water slidethat helps solve problems involving water management in agriculture. The Penman-Monteith FAO56 equation makes it possible to calculate the estimate of the referenceevapotranspiration measure in one day from the hourly data summed over 24 hoursand from daily data.The main objective of this work is to promote a trial regarding the statistical evalua-tion of estimates of evapotranspiration reference evapotranspiration in one day fromthe hourly data summed over 24 hours and from daily data, comparing them throughlinear regression, interval confidence interval, statistical bias, Wilmott concordance in-dex, decorrelation index, confidence index, and performance rating according to theconfidence index. The data used were from all over Brazil, from January 1 to Decem-ber 31 of 2017. The specific objective is to evaluate the estimates of the two methodsunder the quantitative aspect. The results showed that in Brazil the estimated dailyevapotranspiration estimate is overestimated by approximately 8.32% when comparedto the integrated hourly reference evapotranspiration. The South had the highestR2with 0.927, while the Midwest had the lowest with 0.857. The R ranged between 0.963and 0.926, being South and Center-West, respectively. The d ranged from 0.979 to0.960, to the South and the Midwest, respectively. The c also presented higher value inthe South and the lowest in the Center-Oente. For all regions, the performance ratingaccording to the confidence index was ”Great”.Item Planejador de roteiros turísticos: uma aplicação do Problema do Caixeiro Viajante na cidade do Recife(2018) Bispo, Rodolfo César; Cysneiros Filho, Gilberto Amado de Azevedo; http://lattes.cnpq.br/0534822491953359A mobile application (proof of concept) has been developed providing route recommendations for tourists visiting Recife by walking. The tourist selects the points of interest (POI) he wants to visit and the application recommends a route. The tourist can choose the points of interest from a list of points and view them on a map. The application also provides detailed information on points of interest to aid in the choice. Three algorithms were implemented to recommend the route. The algorithms were compared in terms of execution time, impact on the total length of the route generated, memory and CPU usage. The Brute Force algorithm presented a skillful execution time in up to 8 chosen points. Nearest Neighbor moved away more and more of the optimal solution as the number of points increased,while its combination with 2-OPT resulted in an optimization of up to 50 minutes in the route duration.Item Uma abordagem de Game Learning Analytics para identificação de habilidades de leitura e escrita no ensino infantil(2018) Oliveira Neto, José Rodrigues de; Rodrigues, Rodrigo Lins; Amorim, Américo Nobre Gonçalves Ferreira; http://lattes.cnpq.br/7962263612352589; http://lattes.cnpq.br/5512849006877767; http://lattes.cnpq.br/3879751025550218The power that video games have to capture their players’ attention has brought with it the idea of using them with the main objective of reinforcing learning in educational context. Recent studies demonstrate that it is possible to analyze the interactions of players in such games, called Serious Games, to conclude and measure the learning obtained during interaction in those games. Given this context, this work aims to develop an analysis of data obtained from the interaction of players in one game, out of 20, applied during a research that proved their positive impact on the development of reading and writing skills of 4-years-old children. Three classifiers were selected: Naive Bayes, Support Vector Machines (SVM) and Logistic Regression, which were trained with the data resulting from the interaction of these players with the game and demonstrated the hit rate of each of the classifiers. In addition, this work also makes an analysis of the interactions considered more relevant by one of the models, finding relationships between the words proposed as challenge in the test and those present in the game, raising reflections that can be taken into account during the development of a educational game that aims to improve children’s reading and writing skills in early childhood education.Item Um sistema de recomendações de eventos culturais com áudio-descrição(2018) Souza Filho, Robson Ugo Ferreira; Medeiros, Victor Wanderley Costa de; Falcão, Taciana Pontual da Rocha; http://lattes.cnpq.br/5706959249737319; http://lattes.cnpq.br/7159595141911505Around one billion people in the world live with some kind of disability, while almost 24% of the Brazilian population has declared some kind of disability in the 2010 CENSUS. Therefore, the increasingly constant presence of people with visual impairment in cutural spaces has increased and is also due to the presence of communicational accessibility resources. The technological advance has made the disposition of such resources much easier and closer, increasing audience’s autonomy. Based on this argument, this work aims to present the planning and development of an audio-description cultural event recommendation system for blind people using mobile devices, increasing the independence and capacity of the natural indication process to human relationships through collaborative filtering item-based and content-based algorithms. We generated a potential database of these kinds of events, a study about the proposed algorithms and an application usability experiment. The absence of statistical evaluation for the maden evaluations was discovered, since the users’ approach in selecting notes for the events is personal, also resulting from the non-existent consistency of its data.Item Um sistema de apoio à decisão para priorização e estruturação de histórias de usuários: um suporte para equipe ágeis(2018-08) Calado, Alex Rogério da Silva; Garrozi, Cícero; Sampaio, Suzana Cândido de Barros; http://lattes.cnpq.br/0066131495297081; http://lattes.cnpq.br/0488054917286587; http://lattes.cnpq.br/1189807442146524Despite the advances obtained in the Software Engineering with agile methods the market still shows successful rates in unsatisfactory projects. Throughout a software project, changing requirement and priority are unavoidable and recurrent. This is an important factor to the software development businesses mainly small ones with limited resources. The usage of agile techniques such as Scrum and User Stories (US) benefits the businesses and make them more competitive. One of the problems faced with agile requirements is to obtain a prioritization of secure US according to the business value given by the client in agreement with technical specifications. This paper proposes to present an essay that serves as basis to the construction of a decision support tool to the US prioritization decisions. For such was considered a software project as a temporary organization and suggested metrics that best suits the needs of small teams avoiding reworks, the increasing in the deadlines and costs, without disregarding the client satisfaction in interactive deliveries. Based on the Volere requirements template and in the literature review it was proposed the adoption of five metrics to be considered in the prioritization of the User Stories: the client satisfaction in receiving the US and the client dissatisfaction in not receiving it; replacing the traditional value of business; the so usual complexity; the necessity of team learning and; software risks. Using online forms were captured data about these metrics and the prioritization given to each US in software projects that uses the agile Scrum method. Decision Tree was the proposed suggestion for predicting the US prioritization for having a practical visualization and a more intuitive interpretation, facilitating the acceptance as a decision method support for professionals involved in the area. In spite of the low databases volume the results obtained through the Weka tool, like the precision and the ROC curve, were satisfactory without prediction tendencies and with good indexes of correctness, after the algorithms and databases adjustments avoiding overfitting and underfitting.Item Identificação de Outliers para detectar riscos de gestão(2018-08-17) Brizeno, Raissa Costa; Monteiro, Cleviton Vinicius Fonsêca; Lima, Rinaldo José de; http://lattes.cnpq.br/7645118086647340; http://lattes.cnpq.br/9362573782715504; http://lattes.cnpq.br/1672154276438369Outliers are values that doesn’t converge with the rest of the data series. These values when they arise in financial context can represent problems that have a direct influence on the health of an enterprise and the decision-making by the managers. In view of this, it was intended with this work identify anomalies in financial launches arising from the accounts of real companies. For this, statistical analyzes of the launches were fulfilled in order that outliers detection techniques could be chosen and then compared with the outliers detection of evaluators . Among the great variety of techniques were chosen the methods of Boxplot, Boxplot adjusted, MAD and standard deviation. The results show that most of the series didn’t follow a normal distribution, and the experimental results of the comparisons between the automatic methods and the evaluators showed substantial differences.Item Utilização de Game Learning Analytics para verificação do aprendizado em jogo sério voltado ao ensino de zoologia(2019) Farias, Laura Lobo de; Cysneiros Filho, Gilberto Amado de Azevedo; http://lattes.cnpq.br/0534822491953359; http://lattes.cnpq.br/5281211059562823Digital games are tools capable of aiding in learning, games developed exclusively for this purpose are called Serious Games. Serious Games are used in several areas of learning including in the field of Zoology, which aims to train people who know the characteristics of animals and relate ethically with them. However, Serious Games face some problems, one of these difficulties is to understand how players interact with the game and how the learning process takes place. With Game Learning Analytics it is possible to collect and analyze data from the Serious Games with the aim of improving their practical applicability and thus to develop more effective educational games. In this project, we developed a Serious Game capable of assisting learning in Zoology and its effectiveness will be analyzed through the use of Game Learning Analytics.Item Estudo comparativo de algoritmos de classificação supervisionada para classificação de polaridade em análise de sentimentos(2019) Albuquerque, Rotsen Diego Rodrigues de; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/6441716676783585The huge increase of data on the Internet, it is a rich source for public opinion assessment of a specific subject. Consequently, the number of opinions available makes decision-making impossible if it is necessary to read and analyze all opinions. Since the use of Machine Learning has been widely used, I will present a comparative study of two algorithms for classifying movie comments using techniques of natural language processing and Sentiment Analysis. The data obtained were obtained manually where through the competition site called Kaggle where we have about 50,000 comments on various films. The purpose of this study is also to use the concepts of data science and Machine Learning, natural language processing and sentiment analysis to add more information about the entertainment and film industry. That is why these algorithms were created so that it is possible to show the results for this domain in the of movies comments registered in one big site/platform of movie industry, the famous IMDB. After training and testing, the machine had an accuracy of 86 % on predicting sentiments on commented text from movies.Item Métodos de previsão de consumo de energia elétrica residencial em grande volume de dados(2019) Carvalho, Daniel José de; Medeiros, Victor Wanderley Costa de; Gonçalves, Glauco Estácio; http://lattes.cnpq.br/6157118581200722; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/6867315638833821Electricity is one of the primary sources of energy used by humanity. Growing concern for the preservation of the environment has stimulated the search for renewable energy sources capable of reducing impacts on nature. Population growth and the increasingly frequent use of electronic devices in almost all daily activities demand the most efficient use of electricity. Due to these challenges, it is essential to carry out planning to dimen-sion the structure of generation and transmission of electric energy. One of the tools capable of assisting in this sizing is the demand forecasting. Another major challenge in this area lies in the realization of these forecasts in large data scenarios (Big Data). This work aims to evaluate the performance of two prediction methods, ARIMA andHolt-Winters, using temporal series applied to a large volume of data. The database was provided by the DEBS 2014 Grand Challenge event, which contains electricity consumption data for a large number of households for one month. For the application of the prediction methods, we used libraries in the R language. In order to process data,the Apache Spark framework was used in conjunction with the R language to parallelize the data reading processing and filtering parameters. The treated data were convertedin to time series with hourly consumption values, throughout the month comprised by theoriginal database. Predictions were made for the region of the households as a who leand each residence individually. The results showed an advantage of ARIMA versusHolt-Winters in the scenario used, using the RMSE metric as a comparative basis of performance. However, based on similar experiments found in the literature, with due proportions, both RMSE values are within an acceptable range.Item Suporte à decisão multicritério em aplicativos de saúde sob demanda(2019) Pereira, Gustavo Magalhães; Albuquerque Júnior, Gabriel Alves de; Falcão, Taciana Pontual da Rocha; http://lattes.cnpq.br/5706959249737319; http://lattes.cnpq.br/1399502815770584; http://lattes.cnpq.br/6456769669695121Health on demand applications have the main purpose of finding a doctor and take him to your home to provide home care for those who have limited mobility and seek a more convenient medical service, who do not want to face waiting lines and who wish to avoid to go to a hospital to treat basic illnesses. The technological advance has transformed the way traditional services are offered on demand, which is increasingly popular in Brazil. The Federal Council of Medicine (CRM), knowing the impact of technological advances in the practice of medicine, published a resolution No. 2178/2017, which seeks to regulate the operation of applications that offer medical consultation at home. According to the resolution, all applications that offer this service are required to provide a list of physicians available to the patient to choose the best doctor to take care of their case, but the applications do not offer patient assistance in the decision and is in need of a computational solution. In this work was carried out the planning and development of a recommendation system using the methodology to support multi-criteria decision analysis. It was used as a case study the application Clinio, a product of health on demand developed by Epitrack. The solution applied to recommend the best physicians has the purpose of helping the users of the application in choosing the professional that best fits their needs and preferences. To do so, it was used recommendation algorithms to select doctors based on symptoms and geolocation and the Analytical Hierarchical Process (AHP), based on criteria to classify them such as the value of the consultation, the distance between the doctor and the patient, and the age of the physician. The system was implemented using a database of 143 doctors from Pernambuco who work in 10 clinical cases. Through the tests performed it was observed in the recommendation obtained by the users that the system assists in the process of choosing the best professional for a case through the preferences definitions.Item Suporte a decisão no setor sucroalcooleiro(2019) Silva, João Vitor da; Gonçalves, Glauco Estácio; http://lattes.cnpq.br/6157118581200722The sugar and alcohol sector is one of the largest agricultural sectors in Brazil. Each harvest millions of liters of ethanol and thousands of tons of sugar are imported worldwide.Despite the size of the sector, there are several problems that haunt the sugarcane producer. One is the drop in production causing sugar and ethanol production stops.This paper aims to carry out a comparative study of time series forecasting methodsin historical sugarcane production data, together with the construction of operation al indicators to aid in decision making. The database was taken from the quarterly results published by São Martinho for its investors. São Martinho is a publicly traded companyand one of the largest sugar, alcohol and energy production plants in Brazil. The R language was used to carry out the study. The experiments of this work used the predictive model SARIMA, for its almost unanimity in the forecast of agricultural yields.RMSE, ME, and MAE. In the development of the operational indicators, the waste distribution function of the SARIMA model defined along with the forecasts of the modelitself was used.At the end of all the work, the best SARIMA model was obtained for the quarterly sugarcane production data together with the indicators of fall in production: probability off all in production by 30 % and probability of fall in production below quarterly average production.Item Modelo e algoritmos para seleção de sensores como serviço(2019) Verçosa, Nichene Joslin; Gonçalves, Glauco Estácio; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/6157118581200722; http://lattes.cnpq.br/3645909376039196The Internet of Things (IoT) plays a key role in the future of the Internet, as it will inte-grate billions of smart devices which will present sensing, actuation and data processingcapabilities. Thus, each integrated device may have one or more built-in sensors thatwill potentially generate huge amounts of data. This scenario raises the challenge ofefficiently searching and selecting the most appropriate sensors within a set that canpresent similar functionalities and capabilities. In this context, this work presents a math-ematical model for sensor selection able to maximize the attendance to user input re-quirements, such as accuracy, robustness, and availability for different types of sensors(such as temperature, pressure, humidity, wind speed, and so on) in a limited budget.This model was tested through two algorithms, the first being an optimization algorithmand the second a greedy heuristic. These solutions were evaluated and compared interms of three criteria: the optimization time, the amount of budget being utilized, andthe optimal value. The best solutions were found by the optimization algorithm but, thegreedy heuristic found close results. In some cases, the greedy heuristic found solu-tions more than 10x faster when compared to the Optimal Algorithm.Item Avaliação de métodos para interpolação espacial de dados de precipitação(2019) Neris, Airton Martins; Gonçalves, Glauco Estácio; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/6157118581200722; http://lattes.cnpq.br/7254010025661115AbstractInformation on the amount of rainfall is essential for the most varied sectors, such asagriculture and agroforestry. Despite this importance many areas are still not coveredby meteorological stations, which causes the lack of data. To meet this need there aremethods of spatial interpolation, which use the information of correlated points to esti-mate the value that does not exist in a certain area. Thus, this work aims to evaluatemethods for the interpolation of daily precipitation data. The interpolation techniquesused in the experiments were the methods: Inverse Distance Weighting; Ordinary Krig-ing; Random Forest. For the Random Forest two different configurations were used, onethat receives as input the coordinates, and another that receives thebufferdistance,which is one of the most recent pre-processing used in the literature for the RandomForest to estimate its values based on geographical reference. We used rainfall datafrom the 46 meteorological stations from the state of Pernambuco in the period from2013 to 2018, and to compare the precision of the generalization of the methods, weused theleave-one-outcross validation. In the results, the Inverse Distance Weightingpresented a better performance in its estimates, for all the metrics, and the RandomForest using coordinates obtained the second best result. Random Forest usingbufferdistance had a lower result in terms of its metrics, but the quality of visual spatializationproved to be superior by offering a visually smoother result than offered by RandomForest using coordinates.Item Aspect term extraction in aspect-based sentiment analysis(2019) Francisco, Alesson Delmiro; Lima, Rinaldo José de; http://lattes.cnpq.br/7645118086647340The increasing use of the Internet in many directions has created a necessity to analyze alarge quantity of data. A large amount of data is presented as Natural Language Text,which is unstructured, with many ways to express the same information. It is an importanttask to extract information and meaning from those unstructured content, such as opinionson products or services. The need to extract and analyze the large amount of data createdevery day on the Internet surpassed the capabilities of human ability, as a result, manytext mining applications that extract and analyze textual data produced by humans areavailable today, one of such kind of applications is Sentiment Analysis, viewed as a vitaltask both to the academic and commercial fields, so that companies and service providerscan use that knowledge extracted from textual documents to better understand how theircustomers think about them or to know how their products and services are appreciated ornot by their customers. However, the task of analysing unstructured text is a difficult one,that is why it is necessary to provide coherent information and concise summaries to thoserevisions. Sentiment Analysis is the process of computationally identifying and categorizingopinions expressed in a piece of text, especially in order to determine the writer’s attitudetowards a particular topic or product. Aspect-Based Sentiment Analysis is a sub-field ofSentiment Analysis that aims to extract more refined and exact opinions, by breakingdown text into aspects. Most of the current work in the literature does not take profitof either semantic-based resources or NLP-based analysis in the preprocessing stage. Tocountermeasure these limitations, a study on these resources is done aiming to extract thefeatures needed to execute the task, and to make the best combination for ATE. This workhas the main goal of implementing and analysing a method of Aspect Term Extraction(ATE) of users reviews (restaurants and laptops). The proposed method is based on asupervised approach called Conditional Random Fields (CRF) which is able to optimizethe use of features for classification, this choice was justified by previous related work thatdemonstrate the effectiveness of CRF for ATE. Also, we are investigating the existingmethods and features for ABSA, as well as proposing new features and experimentingwith feature combinations in order to find the best features combinations, that are not yetcovered in the state of art. The detailed study is done by experimenting with word features,n-grams and custom made features using an CRF supervised algorithm to accomplish thetask of Aspect Term Extraction with results in terms of Precision, Recall and F-measure,the standard evaluation metrics adopted in the field. Finally, a comparative assessmentbetween the proposal method for ATE against other related work presented in the literaturehas shown that the method presented by this work is competitive.Item Predição de popularidade de podcasts através de características textuais(2019) Santana Júnior, Bernardo de Moraes; Cabral, Giordano Ribeiro Eulalio; http://lattes.cnpq.br/6045470959652684; http://lattes.cnpq.br/9948081717430490With the tremendous growth of Podcasts and the professionalization of its creators,to the point that news networks call this as Podcast’s ”golden age”, new tools have emerged to assist its content producers in building and maintaining of their channels.In this context, finding features inside episodes that provide broader reach to the target audience is of great value to both creators and listeners, allowing channels to stay active longer and offer better content quality.Thus, this paper proposes a study of popularity analysis of brazilian’s podcasts using a podcast audience analysis tool in one of the most used channel and episode aggregators in the world, iTunes. By using Web Scraping tools to collect available and necessary information, also tools for transcriptions of the audios’s episodes in orderto obtain what has been said, and calculating metrics to measure the accuracy of the generated model, therefore making an analysis of which information is relevant or not o predicting a channel’s popularity.Results displayed were favorable in the correlation between the categories analyzed individually and the its text, whereas in an analysis in which categories are not discriminated there is a low relationship between text and popularity, demonstrating that the category of a given channel plays an important role in analyzing its popularity.Item Método para Estimativa de Trajeto Baseado em Dados de Unidades de Medição Inercial(2019) Silva, Lucas Filipe Vieira da; Medeiros, Victor Wanderley Costa de; Gonçalves, Glauco Estácio; http://lattes.cnpq.br/6157118581200722; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/1223833449629855The Internet of Things (IoT) has emerged as a new vision for the Internet, where awide range of devices can connect to the network. This concept is directly related tothe technological advances experienced in the development of semiconductors andintegrated circuits. These devices became cheaper, smaller, and more power efficient.These advances also enable the emergence of new applications, like real-time localiza-tion. Precise location through orientation plays a critical role in estimating the tracking ofa sensor attached to an object. The main objective of this work is to evaluate, through ex-perimentation, a trajectory estimation method based on the gradient descent algorithmand acceleration and rotation data activated by a low-cost Inertial Measurement Unit(IMU). The experiment was performed by collecting data in a straight walk, performed30 times, at a frequency of 100Hz and 50Hz. The equipment used was an MPU-6050sensor coupled to a TTGO T-Beam development board. The localization estimationswere calculated by an algorithm written in Python language. The results have shownthat it is possible to use an IMU to estimate a trajectory performed by a person withreasonable accuracy, adopting a sample rate of 50Hz.Item Um algoritmo para geração de Navigation Meshes em mapas bidimensionais homogêneos: uma aplicação no jogo Dragon Age: Origins(2019) Costa, Ingrid Danielle Vilela; Bocanegra, Silvana; http://lattes.cnpq.br/4596111202208863; http://lattes.cnpq.br/6113606913639280In the field of electronic gaming and more recently in robotics, autonomous agent soften need to repeatedly solve the problem of searching for the smallest path. This need can eventually consume a lot of resources and demands optimizations to make these searches more efficient. Such optimizations may include improvements in search algorithms, map representation, data structures used. This work presents an optimization for search algorithms based on the reduction of the search space by means of an automatic Navigation Meshes generation algorithm which are networks of walka blemap areas implying in a reduction of the search space and consequently improving the search processing time. The generation of Navigation Meshes is a problem with no consolidated solution. To prove the heuristic, path finding problems were solved on 156 benchmark maps. The path findings were performmed by the A* algorithm and the solutions were compared between the original maps and the optimized ones. An average search space reduction of 97.42% was achieved, with a standard deviation of 0.026and the search had an average marginal reduction in execution time of 46.76%.Item Serviço computacional para interpolação espacial de dados meteorológicos(2019) Antonio, Wellington Luiz; Gonçalves, Glauco Estácio; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/6157118581200722; http://lattes.cnpq.br/6454060359445906The spatial interpolation is an essential technique for several fields, such as meteorology, hydrology, agricultural zoning, characterization of health risk areas, sociodemo-graphic, among others. Through interpolation, it is possible to model a surface of a spatially distributed variable from a finite set of known data points. In the case of weather data for agriculture, for instance, interpolation allows us to observe how weather variables behave on a given rural property, which could serve as input for irrigation management on this property. Due to the increasing demand for the use of spatial interpolation,this work proposed the development of a scalable service based on technologies and standards of state-of-the-art in distributed systems, for spatial interpolation of meteorological data associated with agriculture. In order to achieve this goal, we developed a web service based on three different reference evapotranspiration interpolation algorithms, namely: Inverse distance weighted (IDW), Ordinary Kriging (OK) and RandomForest (RF). The first two are widely used algorithms in the spatialization of reference evapotranspiration and they are known to produce low interpolation errors. The third algorithm is originated from Machine Learning. It has been used in recent studies as an alternative for spatial interpolation of environmental variables. This last algorithmhas also been obtaining promising results in the estimation of evapotranspiration. The spatial interpolation web service proposed was developed and its performace was evaluated through measurement. This service was deployed on a production enviromentusing Docker container and a mobile application was developed to integrate and show the main functionalities of the web service. The developed service can be applied inseveral areas. However, in this work more attention was paid to the agricultural sector,as this one is the sector to which this study is focused on. The main beneficiaries of the web service are researchers and developers, which, in turn, are able to develop studies that will benefit the farmer from the application of the service. During this work,we also sought to evaluate how the developed service could be useful for the promotion of the performance and the scalability with respect to spatial interpolation calculus and generation of spatial models. We also highlighted the importance of this software as a support tool for other researches or even for other software, such as Aquaprev, which uses, among other parameters, evapotranspiration and spatial interpolation to estimate the irrigation time of a given crop.Item Análise da utilização de aprendizado de máquina na redução do volume de alertas benignos(2019) Simião, Augusto Fernando de Melo; Soares, Rodrigo Gabriel Ferreira; http://lattes.cnpq.br/2526739219416964; http://lattes.cnpq.br/0529129636604731To aid in combating cyber attacks, Managed Security Services Providers (MSSPs) use SIEMs (Security Information and Event Management). SIEMs are able to aggregate, process and correlate vast amounts of events from different systems, alerting security analysts of the existence of threats, such as computer viruses and cyber attacks, in computer networks. However, SIEMs are known for the high rates of benign alertas (non-threatening alerts) warnings relative to malign alerts (threatening alerts). Due to the high volumes and prevalence of benign alertas, the analyst ignores alerts as a whole, which includes those that represent potential threats, thereby increasing the risk of a network compromise. This phenomenon is known as alert fatigue and has been a frequent target of applying machine learning techniques to reduce the volume of benign alerts. Modern SIEMs use machine learning, in correlation of events, so that only alerts that actually represent possible threats are reported. However, this correlation does not consider the analyst’s deliberation, thus allowing SIEMs to continue to generate alerts previously identified as benign. This paper investigates the use of the algorithms Naïve Bayesian Learning, Decision Tree and Random Forest, to reduce the volume of benign alerts using alerts previously identified by analysts, rather than the chain of events that generate such alerts. In this way, it was possible to show, through experiments, that supervised machine learning techniques can be applied in the identification of alerts previously identified as benign.