Open AccessReview and comparison of works on heterogeneous data and semantic analysis in Big Data
Author(s) -
Vitor Campos,
Jacques Duílio Brancher,
Francyelcyo Pussi Farias,
José Luiz Villela Marcondes Mioni,
Pedro Luiz Garbim Brahim
Publication year - 2021
Publication title -
semina. ciências exatas e tecnológicas
Language(s) - English
Resource type - Journals
eISSN - 1679-0375
pISSN - 1676-5451
DOI - 10.5433/1679-0375.2021v42n1p113
Subject(s) - computer science , big data , data science , information retrieval , data integration , cloud computing , task (project management) , linked data , data extraction , semantic web , data warehouse , digital library , data mining , medline , art , literature , management , poetry , political science , law , economics , operating system
In integration approaches, heterogeneity is one of the main challenging factors on the task of providing integration among different data sources, whose solution lies in the search for equality among them. This work describes the state of the art and theoretical foundation involved in the structural and semantic analysis of heterogeneous data and information. The work aims to review methods and techniques used in data integration in Big Data, considering data heterogeneity, reviewing techniques that use the concepts of Semantic Web, Cloud Computing, Data Analysis, Big Data, Data Warehouse and other technologies to solve the problem of data heterogeneity. The research was divided into three stages. In the first stage, articles were selected from digital libraries according to their titles and keywords. In the second stage, the papers went through a second filter based on their summary, and, besides that, duplicate articles were also removed. The works’ introduction and conclusion were analyzed in the third stage to select the articles belonging to this systematic review. Throughout the study, articles were analyzed, compared and categorized. At the end of each section, the interrelationships and possible areas for future work were shown.