Tarlis Tortelli
Portela


Universidade Tecnológica Federal do Paraná (UTFPR) – Campus Dois Vizinhos
PhD in Computer Science (UFSC / UniPi / CNR)
Tarlis Tortelli Portela is a professor at the Federal Technological University of Paraná (UTFPR), Campus Dois Vizinhos. Former professor at the Federal Institute of Education, Science and Technology of Paraná (IFPR), Campus Palmas, for 13 years. PhD in Computer Science at the Federal University of Santa Catarina (UFSC) in cotutela with University of Pisa (UniPI) and Institute of Information Science and Technologies “Alessandro Faedo” – National Research Council of Italy (ISTI-CNR). Master’s in Electrical Engineering in the Postgraduate Program in Electrical Engineering by the Federal Technological University of Paraná (UTFPR), Campus Pato Branco. Specialist in Software Engineering and Project Management at Faculdade Mater Dei and specialist in Computer Networks at UTFPR. Graduate in Technology in Information Systems (UTFPR). Has experience as a software architect, engineer, developer, and business manager. Also acted as Course Coordinator and Internship Coordinator.
· data mining · spatio-temporal data · multidimensional data · sequential data · classification · trajectories · GIS ·
Mobility, Sequential, and Multidimensional Data Mining
Research in this area focuses on extracting meaningful patterns and insights from large-scale spatiotemporal datasets, such as human or animal mobility traces, sensor data, and user interactions. It involves techniques that address the high dimensionality and spatio-temporal dependencies inherent to this data.
Methods for Trajectory Data Analysis
This research encompasses the development and application of analytical methods to understand movement patterns over space and time. Methods for analysis include trajectory classification, similarity measures, clustering, segmentation, and summarization, with applications in transportation, urban planning, and behavioral analysis.
Artificial Intelligence for Mobility Data Analysis
This topic involves the application of AI techniques to analyze and interpret mobility data, machine learning and large language models for analysis, behavior understanding and pattern discovery.
Public research and personal projects
HIgh-PERformance movelet extraction for trajectory classification (2022).
Efficient Movelet Extraction for Multiple Aspect Trajectory Classification (2024).
Python Framework for Multiple Aspect Trajectory Data Mining.
Datasets for Multiple Aspect Trajectories.
Generic pagination component for Dash.
Class diary, created for IFPR campus Palmas-PR.
Reservation system for rooms and labs (IFPR).
App for Systematic Literature Reviews. Use: slr.tarlis.com.br
Introductory Data Mining lecture (slides and practical examples).