Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

Spatial data mining is an active topic in spatial databases. This paper proposes a new
clustering method for moving object trajectories databases. It applies specifically to …

Joining trajectory datasets is a significant operation in mobility data analytics and the
cornerstone of various methods that aim to extract knowledge out of them. In the era of Big …

The i4sea research project provides effective and efficient big data integration, processing,
and analysis technologies to deliver both real-time and historical operational snapshots of …

This chapter addresses the case of multivariate time-series clustering and utilizes
spatiotemporal data as specific application domain of interest. Time-series data is one of the …

Data analysis based on the similarity of vehicle trajectories in a vehicular network is
emerging as a new exciting paradigm that is important for law enforcement applications (eg …

Smartphones are nowadays equipped with a number of sensors, such as WiFi, GPS,
accelerometers, etc. This capability allows smartphone users to easily engage in …

In this demonstration paper, we present a powerful distributed framework for finding similar
trajectories in a smartphone network, without disclosing the traces of participating users. Our …

In this paper, we propose a new spatio-temporal similarity measure to compute spatio-
temporal relevance between two trajectories of moving objects on road networks, which is …

Driven by the fast development of mobile internet and mobile devices, huge volumes of
trajectory data describing the spatial-temporal information of moving objects are currently …