I Congresso de Execução Fiscal da OAB/SC: Desafios Atuais para eficiência da Execução Fiscal Municipal


21h: Aplicação da Inteligência Artificial no gerenciamento do executivo fiscal.
Palestrante: Dr. Sérgio Mariano Dias
Mediadora: Dra. Elaine Goncalves Weiss de Souza


Data Science – Técnicas e Tecnologias Aplicadas em Governo

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.



Extraction of qualitative behavior rules for industrial processes from reduced concept lattice

Minimal implications base for social network analysis

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

Identification of substructures in complex networks using formal concept analysis

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

Professional Competence Identification Through Formal Concept Analysis


As the job market has become increasingly competitive, people who are looking for a job placement have needed help to increase their competence to achieve a job position. The competence is defined by the set of skills that is necessary to execute an organizational function. In this case, it would be helpful to identify the sets of skills which is necessary to reach job positions. Currently, the on-line professional social networks are attracting the interest from people all around the world, whose their goals are oriented to business relationships. Through the available amount of information in this kind of networks it is possible to apply techniques to identify the competencies that people have developed in their career. In this scenario it has been fundamental the adoption of computational methods to solve this problem. The formal concept analysis (FCA) has been a effective technique for data analysis area, because it allows to identify conceptual structures in data sets, through conceptual lattice and implications. A specific set of implications, know as proper implications, represent the set of conditions to reach a specific goal. So, in this work, we proposed a FCA-based approach to identify and analyze the professional competence through proper implications.


Formal concept analysis Proper implications Professional competence On-line social networks 

Silva P.R., Dias S.M., Brandão W.C., Song M.A., Zárate L.E. (2018) Professional Competence Identification Through Formal Concept Analysis. In: Hammoudi S., Śmiałek M., Camp O., Filipe J. (eds) Enterprise Information Systems. ICEIS 2017. Lecture Notes in Business Information Processing, vol 321. Springer, Cham

Journal of Big Data

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.