The team with Professor Manos Papagelis (second from left)
Lassonde School of Engineering graduate students Abdullah Sawas, Abdullah Abuolaim and Mahmoud Afifi, under the supervision of Professor Manos Papagelis, received the best paper award at the 19th Institute of Electrical and Electronics Engineers International Conference on Mobile Data Management (IEEE MDM 2018). The group's paper is titled “Tensor Methods for Group Pattern Discovery of Pedestrian Trajectories.”
An overview of the system architecture. The flow chart within the dashed line depicts the paper’s contribution
The IEEE MDM series of conferences is a prestigious forum for the exchange of innovative and significant research results in mobile data management. This year’s event took place at the end of June in Aalborg, Denmark.
A snapshot of the system's user interface that allows interactive exploration of pedestrian trajectory group patterns
Mining trajectory data of moving objects to find interesting patterns is of increased research interest due to a broad range of useful applications, including analysis of transportation systems, location-based services and crowd behaviour analysis. The students' paper presents tensor-based methods for discovering group patterns of moving objects. Group pattern mining describes a special type of trajectory data mining that requires to efficiently discover trajectories of objects that are found near each other for a period of time, such as pedestrians walking together.
For project details, sample videos and an online interactive demonstration, visit sites.google.com/view/pedestrians-group-pattern.
Papagelis’ research interests include data mining, graph mining, machine learning, big data, knowledge discovery and city science.
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