Spatial-Time Motifs Discovery

Student: Heraldo Pimenta Borges Filho (
Advisor: Eduardo Ogasawara (
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 of data. The package offers a way to do this research quickly and efficiently.
Code repository at Git-Hub:
Acknowledgments: The authors thank CAPES, CNPq, and FAPERJ for partially sponsoring this work.
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.

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