Acompanhe as disciplinas de Aprendizado de Máquina e Projeto Integrado em Big Data da Pós-graduação em Ciência de Dados e Big Data da PUC Minas no menu “teaching”.
Aims and scope
The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material.
Sérgio Mariano Dias, com colaboração de Gustavo Torres e Marcelo Pita, da divisão de Soluções Analíticas no Data Lake — 11 de dezembro de 2017
O que é ciência de dados? Qual o perfil e as competências do cientista envolvido nesse novo paradigma?
Best Student Paper Award in area of Databases and Information Systems Integration for the paper entitled: Formal Concept Analysis applied to Professional Social Networks Analysis, 19th International Conference on Enterprise Information Systems (ICEIS). http://www.iceis.org/PreviousAwards.aspx?y=2018
Abstract: From the recent proliferation of online social networks, a set of specific type of social network is attracting more and more interest from people all around the world. It is professional social networks, where the users’ interest is oriented to business. The behavior analysis of this type of user can generate knowledge about competences that people have been developed in their professional career. In this scenario, and considering the available amount of information in professional social networks, it has been fundamental the adoption of effective computational methods to analyze these networks. The formal concept analysis (FCA) has been a effective technique to social network analysis (SNA), because it allows identify conceptual structures in data sets, through conceptual lattice and implication rules. Particularly, a specific set of implications rules, know as proper implications, can represent the minimum set of conditions to reach a specific goal. In this work, we proposed a FCA-based approach to identify relations among professional competences through proper implications. The experimental results, with professional profiles from LinkedIn and proper implications extracted from PropIm algorithm, shows the minimum sets of skills that is necessary to reach job positions.
Silva, P., Dias, S., Brandão, W., Song, M. and Zárate, L. Formal Concept Analysis Applied to Professional Social Networks Analysis. In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) – Volume 1, pages 123-134. ISBN: 978-989-758-247-9 Copyright © 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
Seminário: Aplicação de Ciência de Dados e Big Data nas Empresas
Título da apresentação: Big Data Analytics em Governo
Apresentação realizada na PUC Minas em 29/04/2017
Formal concept analysis (FCA) is a mathematical theory of data analysis with applications in many areas. The problem of obtaining a concept lattice of an appropriate size was identified in several applications as one of the most important problems of FCA. In order to deal with this problem several techniques with different characteristics were proposed for concept lattice reduction. However, there are currently no adequate methods to assess what types of knowledge transformations can result from a reduction. A methodology for analysis of concept lattice reduction is presented here. It is based on the use of sets of proper implications holding in the original and reduced formal contexts or concept lattices. Working with both sets of implications, the methodology is able to show what is preserved, eliminated, inserted or transformed by a reduction technique. Three classes of reduction techniques are analyzed from the standpoint of the methodology in order to highlight techniques of each class have in common with respect to the transformations performed. Such analysis is followed by specific examples in each class.
- Formal concept analysis;
- Lattice reduction;
- Proper implications
Dias, Sérgio M.; Vieira, N. J. A methodology for analysis of concept lattice reduction, Information Sciences, Volume 396, August 2017, Pages 202-217, ISSN 0020-0255, http://dx.doi.org/10.1016/j.ins.2017.02.037.