High Performance Intelligent Decision Systems


About our research group

Our research group works on design and implementation of decision support systems based on intelligent algorithms to work on high performance computer architectures. The goal of such algorithms is to solve semi-structured data mining and optimization problems. The researchers involved in this project, which are experts in complementary fields, intend to work together to combine their research to advance and contribute to the field of decision support systems. Such systems are extremely important to organizations as they provide valuable information that helps managers take strategic decisions.

Decision support systems must be able to deal with semi-structured problems and to quickly extract reliable information from large sources of data. To solve semi-structured problems, non-traditional algorithms must be employed, such as clustering, classification and optimization algorithms based on bio-inspired computing.

Due to the large set of parameters, queries and data associated with semi-structured problems, algorithms applied to such problems often require large computational power to obtain results. Therefore, the application of high performance computing techniques may lead to faster responses in such situations, so that these algorithms can be successfully applied to aid decision-making.

Our group considers innovation as the basis of the whole research project, due not only to the integration of distinct areas of Computer Science, but also to the focus in the development of products based on innovative concepts.


Published Works

Scientific and Technological Production

Check our scientific and technological production (content in Portuguese)


Check the material we use for our research

Our Team


A. L. S. Gradvohl

Post-Doc in Distributed Systems

A. E. A. Silva

Ph.D. in Electrical Engineering

G. P. Coelho

Ph.D. in Electrical Engineering

Graduate Students

Supervised by Prof. A. E. A. Silva

Abraão Zaidan

Intelligent decision suport system to collect and analyze antecipative information

Pedro Nunes

Automatização de Metodologia de gerenciamento de sinais fracos

Thiago Andrade

Melhoria na detecção de mudanças de conceito em fluxos de dados online

Supervised by Prof. A. L. S. Gradvohl

Fernanda Ribeiro

Improvements on solar flares forecasting using x-rays time series

Luís Fernando Lopes Grim

A Complex Event Processing distributed system for time series forecasting applied to air quality prediction

Matheus B. de Moraes

Evaluation of data reduction techniques applied to concept drift scenarios in online data streams

Rafael Sanches Rocha

Development of a system based on mobile devices to support the victims of natural disasters

Tiago Cinto

Space Weather and Machine Learning: contributions to the prediction of solar flares C, M and X using neural networks and statistical analysis

Supervised by Prof. G. P. Coelho

Ederson Borges

Information Theoretic-based Machine Learning applied to Bioinspired Algorithms

Jorge Barajas

Implementation of concept drift detection algorithms to improve the particulate matter forecasting based on machine learning tools

Romulo Souza

Desenvolvimento de Meta-heurísticas para Otimização de Estratégias de Produção para Campos de Petróleo

Undergraduate students

Supervised by Prof. A. E. A. Silva

Jéssica Pereira

Exploration of association rules for attributes of solar explosions via agglomerative hierarchical clustering

Supervised by Prof. A. L. S. Gradvohl

Matheus Dias Queiroz

Study of the man in the middle attack in IPv4/IPv6 transition scenarios

Matheus Evers Rodrigues Fernandes

Using Deep Learning to predict solar explosions using magnetograms

Supervised by Prof. G. P. Coelho

Bruno Antunes Carneiro da Silva

Self-organizing Maps for Solar Flare Forecasting

Mirelle Candida Bueno

A Comparative Study on Hierarchical Clustering for Solar Flare Forecasting

Former graduate students

Supervised by Prof. A. E. A. Silva

Rene Argento

Aplicação de técnicas de ensemble para previsão de ciclos solares

Ricardo Barbosa

Mineração de texto aplicada a estórias de metodologias ágeis

Supervised by Prof. G. P. Coelho

Felipe Oriani

Recommendation System for Buying and Selling on Stock Market using Ensembles

Former undergraduate students

Supervised by Prof. A. E. A. Silva

Ismael Caldana

Solar explosion forecasting through time-series of X-ray fluxes via MLP neural nets

Lucas Tonon

Desenvolvimento de Rastreador Web de Sinais Fracos

Supervised by Prof. A. L. S. Gradvohl

Thaís Teche

Analysis of the Multi-layer Perceptrons applied to solar flares forecasting

Vinícius dos Santos

Study of the potential of SunPy for of solar data analysis in Python

Supervised by Prof. G. P. Coelho

Douglas Araújo

Feature Selection for Solar Flare Forecast based on Evolutionary Algorithms

Gabriel Barros

Study about the Impact of Meteorological Variables in the Prediction of the Concentration of Particulated Matter

João Victor Ignácio

Study of solar flares using data clustering

Victor Pedrazzi

Predicting Solar Activity via Artificial Neural Networks Applied to Flow Data of X-Rays

Software Development

The following software were registered at the Brazilian National Institute of Industrial Property (INPI).

  • Astronomus (Register 13168-3)

  • B2-4CEP - Benchmark Tool for Complex Event Processing systems (Register BR512016001623-0)

  • CONsensus (Register BR512014000362-0)

  • Gaspra - Gerador de experimentos para imagens astronômicas (Register BR512013000117-0)

  • StockMOS - Stock Market Operation System (Register BR5120160009-0)


Project Partners

Center for Petroleum Studies

DEsign, Verification and VAlidation of large scale, dynamic Service SystEmS

José Roberto Cecatto

National Institute for Space Research - INPE

São Paulo Astronomy Network - (SPAnet)

Sponsor Institutions

We would like to thank the following institutions that donated computing resources to our research

Microsoft Azure


Hasso Plattner Institute - Future SOC Lab

Contact Us

Should you have any questions, please contact us!