21h: Aplicação da Inteligência Artificial no gerenciamento do executivo fiscal.
Palestrante: Dr. Sérgio Mariano Dias
Mediadora: Dra. Elaine Goncalves Weiss de Souza
Apresentação no TDC Innovation: O Governo é um Big User em Data Science! Um grande produtor e consumidor de dados em ciência de dados. Do modelo relacional ao lago de dados demonstramos nesta palestra técnicas e tecnologias utilizadas pelo governo nos últimos doze anos para lidar com grandes volumes de dados em projetos estratégicos para o país. Como processar trezentos milhões de documentos no formato XML por mês ou manipular documentos hierárquicos com Gigabytes de informação? Estes são alguns dos exemplos abordados sobre a perspectiva de técnicas e tecnologias em ciência de dados.
TDC INNOVATION – DESAFIOS PARA CRIAÇÃO DO FUTURO DIGITAL
Article type: Research Article
Authors: Dias, Sérgio M.a; * | Zárate, Luis E.a | Song, Mark A.J.a | Vieira, Newton J.b | Kumar, Ch. Aswanic
Affiliations: [a] Department of Computer Science, Pontifical Catholic University of Minas Gerais, Minas Gerais, Brazil | [b] Federal University of Minas Gerais, Minas Gerais, Brazil | [c] School of Information Technology and Engineering, VIT University, Vellore, India
Correspondence: [*] Corresponding author: Sérgio M. Dias, Department of Computer Science, Pontifical Catholic University of Minas Gerais, Minas Gerais, Brazil. E-mail: firstname.lastname@example.org.
Abstract: Formal concept analysis (FCA) become an alternative approach to extract and represent knowledge of real world systems. That knowledge can be obtained from implications rules extracted of concept lattices formed by ordered formal concepts. However, in complex systems the number of formal concepts can be large. To deal with this complexity of the FCA, concept reduction techniques can be applied in order to balance the quality of information, and the computational cost for generating and handling the lattice. In this paper, we develop a novel approach to represent the behavior of physical processes through qualitative rules based on proper implications (minimum representation of the data) extracted from the reduced concept lattice. As a case study, the cold rolling process was considered. This process characterize by the strong non-linearity among its parameters. The results show that the qualitative behavior of the rolling process is preserved even when the reduction techniques are applied. The approach can be used to understand the relationship between process parameters through implication rules under different operating conditions of a process. The paper discusses some generic procedures that can be adapted to apply this approach to other industrial processes.
Keywords: Formal concept analysis, proper implications, cold rolling process
Journal: Intelligent Data Analysis, vol. 24, no. 3, pp. 643-663, 2020
Democratizando a Inteligência Artificial – Soluções de IA são encontradas em diferentes produtos, mas sua utilização ainda é limitada a especialistas com habilidades e conhecimentos muito específicos
Trilha de aprendizagem “Introdução à ciência de dados” desenvolvida para disseminar os temas ciência de dados, mineração de dados e inteligência artificial no Serpro.
Currently, social network (SN) analysis is focused on the discovery of activity and social relationship patterns. Usually, these relationships are not easily and completely observed. Therefore, it is relevant to discover substructures and potential behavior patterns in SN. Recently, formal concept analysis (FCA) has been applied for this purpose. FCA is a concept analysis theory that identifies concept structures within a data set. The representation of SN patterns through implication rules based on FCA enables the identification of relevant substructures that cannot be easily identified. The authors’ approach considers a minimum and irreducible set of implication rules (stem base) to represent the complete set of data (activity in the network). Applying this to an SN is of interest because it can represent all the relationships using a reduced form. So, the purpose of this paper is to represent social networks through the steam base.
The authors’ approach permits to analyze two-mode networks by transforming access activities of SN into a formal context. From this context, it can be extracted to a minimal set of implications applying the NextClosure algorithm, which is based on the closed sets theory that provides to extract a complete, minimal and non-redundant set of implications. Based on the minimal set, the authors analyzed the relationships between premises and their respective conclusions to find basic user behaviors.
The experiments pointed out that implications, represented as a complex network, enable the identification and visualization of minimal substructures, which could not be found in two-mode network representation. The results also indicated that relations among premises and conclusions represent navigation behavior of SN functionalities. This approach enables to analyze the following behaviors: conservative, transitive, main functionalities and access time. The results also demonstrated that the relations between premises and conclusions represented the navigation behavior based on the functionalities of SN. The authors applied their approach for an SN for a relationship to explore the minimal access patterns of navigation.
The authors present an FCA-based approach to obtain the minimal set of implications capable of representing the minimum structure of the users’ behavior in an SN. The paper defines and analyzes three types of rules that form the sets of implications. These types of rules define substructures of the network, the capacity of generation users’ behaviors, transitive behavior and conservative capacity when the temporal aspect is considered.
Paula Raissa, Sérgio Dias, Mark Song, Luis Zárate, (2018) “Minimal implications base for social network analysis”, International Journal of Web Information Systems, Vol. 14 Issue: 1, pp.62-77, https://doi.org/10.1108/IJWIS-04-2017-0028
Sebastião M. Neto, Sérgio Dias, Rokia Missaoui, Luis Zárate, Mark Song, (2018) “Identification of substructures in complex networks using formal concept analysis”, International Journal of Web Information Systems, Vol. 14 Issue: 3, pp.281-298, https://doi.org/10.1108/IJWIS-10-2017-0067
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.