Journal of Information and Data Management最新文献_第3页

Analysis of ENEM’s attendants between 2012 and 2017 using a clustering approach 使用聚类方法分析2012年至2017年ENEM的服务人员

Journal of Information and Data Management Pub Date : 2021-02-14 DOI: 10.5753/jidm.2020.2023

Afonso Matheus Sousa Lima, Alexander Ylnner Choquenaira Florez, Alexis Iván Aspauza Lescano, João Victor De Oliveira Novaes, Natalia De Fatima Martins, C. Traina Junior, Elaine Parros Machado de Sousa, José Fernando Rodrigues Junior, Robson Leonardo Ferreira Cordeiro

{"title":"Analysis of ENEM’s attendants between 2012 and 2017 using a clustering approach","authors":"Afonso Matheus Sousa Lima, Alexander Ylnner Choquenaira Florez, Alexis Iván Aspauza Lescano, João Victor De Oliveira Novaes, Natalia De Fatima Martins, C. Traina Junior, Elaine Parros Machado de Sousa, José Fernando Rodrigues Junior, Robson Leonardo Ferreira Cordeiro","doi":"10.5753/jidm.2020.2023","DOIUrl":"https://doi.org/10.5753/jidm.2020.2023","url":null,"abstract":"Data analysis is increasingly being used as an unbiased and accurate way to evaluate many aspects of society and their evolution over the years. This article presents an analysis of student’s characteristics, between 2012 and 2017, in the most important exam for entry into higher education in Brazil, the Exame Nacional do Ensino Médio (ENEM). The intention is to gain insights of Brazilian regions, ENEM’s areas of knowledge, type of school and accessibility, using a clustering method (K-means). An extensive and careful cleaning of the database was made in order to homogenize it and avoid types of statistical bias. The results of this work are presented objectively in the article, so it may be useful and used as a numerical base in works of socio-educational disciplines or studies that are interested in better understanding the evolution of ENEM in recent years. Finally, some discussions and restrictions on grouping results were presented in a timely manner.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130601753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Processing of Analytical Queries Extended with Similarity Search Predicates over Images in Spark 基于相似搜索谓词扩展的分析查询在Spark中的高效处理

Journal of Information and Data Management Pub Date : 2020-12-30 DOI: 10.5753/jidm.2020.2019

Guilherme Muzzi da Rocha, Cristina Dutra de Aguiar Ciferri

{"title":"Efficient Processing of Analytical Queries Extended with Similarity Search Predicates over Images in Spark","authors":"Guilherme Muzzi da Rocha, Cristina Dutra de Aguiar Ciferri","doi":"10.5753/jidm.2020.2019","DOIUrl":"https://doi.org/10.5753/jidm.2020.2019","url":null,"abstract":"An image data warehousing extends a conventional data warehousing to also manipulate images represented by feature vectors and attributes for similarity search. A challenge that arises is the efficient processing of analytical queries extended with a similarity search predicate. These queries have a high computational cost since they require the processing of costly star join operations and distance calculations in the same setting. We consider applications that manage huge volumes of data, where the use of parallel and distributed data processing frameworks is needed. In this article, we introduce two methods to efficiently solve this challenge in Spark. BrOmnImg is based on the integration of the broadcast join and the Omni techniques for the processing of the star join operation and the distance calculations, respectively. BrOmnImgCF extends BrOmnImg by using the conventional predicate to further reduce the number of distance calculations. Compared with the closest method available in the literature, BrOmnImg reduced the time spent on query processing by up to about 65%. Compared with BrOmnImg, BrOmnImgCF improved the performance by up to about 54%.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122802187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial analysis and data mining of urban trees 城市树木空间分析与数据挖掘

Journal of Information and Data Management Pub Date : 2020-10-30 DOI: 10.5753/jidm.2020.2022

Gabriel O. C. Pacheco, Clodoveu A. Davis

引用次数: 0

PrivLBS: Preserving Privacy in Location-Based Services PrivLBS:保护基于位置的服务中的隐私

Journal of Information and Data Management Pub Date : 2020-02-19 DOI: 10.5753/jidm.2019.2038

Eduardo R. Duarte Neto, André L. C. Mendonça, Javam C. Machado

引用次数: 0

Feature selection and comparison of classifiers for predicting protein class 蛋白质分类器的特征选择与比较

Journal of Information and Data Management Pub Date : 2019-12-30 DOI: 10.5753/jidm.2019.2034

B. C. Santos, Cora Silberschneider, M. W. Rodrigues, C. L. N. Pinto, C. Nobre, Luis E. Zárate

{"title":"Feature selection and comparison of classifiers for predicting protein class","authors":"B. C. Santos, Cora Silberschneider, M. W. Rodrigues, C. L. N. Pinto, C. Nobre, Luis E. Zárate","doi":"10.5753/jidm.2019.2034","DOIUrl":"https://doi.org/10.5753/jidm.2019.2034","url":null,"abstract":"Knowing the function of proteins is essential for understanding several biological systems. The experiments in laboratory to determine protein class are costly and require a long time to be done. Therefore, it is necessary to provide efficient computational models to identify the class to which a protein belongs. Nowadays, a significant volume of information regarding proteins and their structure is continually being made available in public data repositories. For example, the STING_DB database has a lot of information extracted from all protein structural levels (primary, secondary, tertiary, and quaternary), which are frequently used in classification models for this type of problem. However, it is unknown which physical-chemical properties are the most relevant ones to contribute to the prediction of the class. Therefore, there is a need to identify the subset of more suitable properties. In this work, we propose an approach based on a multi-objective genetic algorithm with the classifier k-NN to select the best physical-chemical properties. Our strategy uses a multi-objective genetic algorithm to obtain a smaller subset of features that contribute significantly to the prediction problem. To improve the prediction’s performance, we choose to perform a post enrichment process, then we compare the performance of our methodology with several classifiers: ANN, SVM, Random Forest, and k-NN. Our method achieved an average F-measure value of 70.22% with the Random Forest classifier. Finally, a comparative analysis, with statistical significance, shows the relevance of our approach in relation to other methodologies.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129365473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recommending Stores for Shopping Mall Customers with RecStore RecStore为购物中心客户推荐商店

Journal of Information and Data Management Pub Date : 2018-12-30 DOI: 10.5753/jidm.2018.2040

Diogo V. de S. Silva, Renato De S. Silva, Frederico A. Durão

引用次数: 0

An Algebra for Modeling and Simulation of Continuous Spatial Changes 连续空间变化的建模与仿真代数

Journal of Information and Data Management Pub Date : 2018-12-30 DOI: 10.5753/jidm.2018.2045

André Fonseca Amâncio, Tiago Garcia de Senna Carneiro

引用次数: 1

Identifying Finest Machine Learning Algorithm for Climate Data Imputation in the State of Minas Gerais, Brazil 确定巴西米纳斯吉拉斯州气候数据输入的最佳机器学习算法

Journal of Information and Data Management Pub Date : 2018-12-30 DOI: 10.5753/jidm.2018.2044

Lucas O. Bayma, Marconi A. Pereira

引用次数: 2

Using Car to Infrastructure Communication to Accelerate Learning in Route Choice 利用汽车基础设施通信加速路径选择学习

Journal of Information and Data Management Pub Date : 1900-01-01 DOI: 10.5753/jidm.2021.1935

Guilherme D. dos Santos, Ana L. C. Bazzan, Arthur Prochnow Baumgardt

{"title":"Using Car to Infrastructure Communication to Accelerate Learning in Route Choice","authors":"Guilherme D. dos Santos, Ana L. C. Bazzan, Arthur Prochnow Baumgardt","doi":"10.5753/jidm.2021.1935","DOIUrl":"https://doi.org/10.5753/jidm.2021.1935","url":null,"abstract":"The task of choosing a route to move from A to B is not trivial, as road networks in metropolitan areas tend to be over crowded. It is important to adapt on the fly to the traffic situation. One way to help road users (driver or autonomous vehicles for that matter) is by using modern communication technologies.In particular, there are reasons to believe that the use of communication between the infrastructure (network), and the demand (vehicles) will be a reality in the near future. In this paper, we use car-to-infrastructure (C2I) communication to investigate whether the road users can accelerate their learning processes regarding route choice by using reinforcement learning (RL). The kernel of our method is a two way communication, where road users communicate their rewards to the infrastructure, which, in turn, aggregate this information locally and pass it to other users, in order to accelerate their learning tasks. We employ a microscopic simulator in order to compare this method with two others (one based on RL without communication and a classical iterative method for traffic assignment). Experimental results using a grid and a simplification of a real-world network show that our method outperforms both.","PeriodicalId":293511,"journal":{"name":"Journal of Information and Data Management","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128209361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2