About Eduardo Ogasawara

I am a Professor of the Computer Science Department of the Federal Center for Technological Education of Rio de Janeiro (CEFET / RJ) since 2010. I hold a PhD in Systems Engineering and Computer Science at COPPE / UFRJ. Between 2000 and 2007 I worked in the Information Technology (IT) field where I acquired extensive experience in workflows and project management. I have solid background in the Databases and my primary interest is Data Science. He currently studies space-time series, parallel and distributed processing, and data preprocessing methods. I am a member of the IEEE, ACM, INNS, and SBC. Throughout my career I have been presenting consistent number of published articles and projects approved by the funding agencies, such as CNPq and FAPERJ. I am also reviewer of several international journals, such as VLDB Journal, IEEE Transactions on Service Computing and The Journal of Systems and Software. Currently, I am heading the Post-Graduate Program in Computer Science (PPCIC) of CEFET / RJ.
Website: http://eic.cefet-rj.br/~eogasawara
eogasawara has written 99 articles so far, you can find them below.

Flight delay review

Systematic Review Data

The systematic review data for the paper On the Relevancy of Data Science for Flight Delay Research can be found at survey-analysis.xls.

Reproducibility

The possibility for the reader to be able to reproduce all the results presented in papers is significant for the scientific method. Initiatives that publishes methods and experimental evaluation using active documents (such as Jupyter notebook) are relevant for support reproducibility. We have provided an example (analytics-example.ipynb) of a reproducible code that enables the comprehension of some data analytics methods presented in the paper.

 

 

Hands-on short-course on Data Analysis

Goal: The Data Analysis workshop encompasses a set of data mining techniques aimed at extracting knowledge from data. The process of knowledge extraction includes exploratory data analysis, preprocessing and prediction. This short course is contextualized using the R language. Proposal: Introduction Basics of R Exploratory data analysis Data Preprocessing Regression Slides Code examples: Basics of […]
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Short course on Data Analysis

Study of data mining techniques, i.e., extraction of knowledge from large volumes of data. The knowledge extraction process includes exploratory analysis, data preprocessing, clustering, and prediction. This short-course is regularly offered once a year at LNCC under the collaboration between CEFET/RJ and LNCC. Fill this form to request access to the course. Slides and schedule available […]
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Spatial-Time Motifs Discovery

Authors: Heraldo Borges, Murillo Dutra,  Rafaelli~Coutinho, Fábio Perosi, Amin Bazaz,  Florent~Masseglia, Esther Pacitti, Fábio Porto, Eduardo Ogasawara Abstract: Discovering motifs in time series data has been widely explored. Various techniques have been developed to tackle this problem. However, when it comes to spatial-time series, a clear gap can be observed according to the literature review. […]
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Oportunidades na Ciência da Computação: Uma visão na perspectiva de Ciência de Dados

Título: Oportunidades na Ciência da Computação: Uma visão naperspectiva de Ciência de Dados Fórum: Escola Municipal Victor Hugo Data: December / 2018 Local: Rio de Janeiro, RJ Resumo: O Brasil atualmente ocupa o sexto maior mercado mundial de tecnologia da informação e comunicação (TIC) (ABES 2016). Estima-se que o setor de TIC tenha movimentado US$ 152 […]
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Comparing Motif Discovery Techniques with Sequence Mining in the Context of Space-Time Series

Title: Comparing Motif Discovery Techniques with Sequence Mining in the Context of Space-Time Series Venue: INRIA / LIRMM / University of Montpellier Date: November / 2018 Location: Montpellier, France Abstract: A relevant area that is being explored in time series analysis community is finding patterns. Patterns are sub-sequences of time series that are related to some special […]
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Spatial-Time Motifs Discovery

Student: Heraldo Pimenta Borges Filho (heraldoborges@gmail.com) Advisor: Eduardo Ogasawara (eogasawara@ieee.org) Description: The package STMotif allows performing research of motif in spatial-time series. A motif is a previously unknown subsequence of a (spatial) time series with a relevant number of occurrences. The main purpose is to find a way to handle the issue of large amounts […]
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LADaS 2018 Workshop

The LADaS 2018 Workshop (Latin America Data Science Workshop) was organized in conjunction with the VLDB 2018 (Very Large Data Bases) at Rio de Janeiro on August 27th. Scope: Dealing with the data deluge produced nowadays in different areas, ranging from basic sciences to billions of users of Global Internet services, emerges as one of […]
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Detecção de Anomalias Frequentes no Transporte Rodoviário Urbano

Title: Detecção de Anomalias Frequentes no Transporte Rodoviário Urbano Venue: SBBD 2018 Date: August / 2018 Location: Rio de Janeiro, RJ – Brasil Abstract: The growth of urban population and, consequently, the number of vehicles causes the increase of traffic jams and emission of polluting gases. In this context, we observe the intensification of papers that aim […]
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Rumo à Integração da Álgebra de Workflows com o Processamento de Consulta Relacional

Title: Rumo à Otimização de Operadores sobre UDF no Spark Venue: SBBD 2018 Date: August / 2018 Location: Rio de Janeiro, RJ – Brasil Abstract: Workflows emerged as a basic abstraction for structuring data analysis experiments in the current Data Intensive Scalable Computing (DISC) scenario. In many situations, these workflows are intensive, either computationally or in relation […]
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