Clustering is an efficient way to group data into different classes on basis of the internal and
previously unknown schemes inherent of the data. With the development of the location …

The increasing use of location-aware devices has led to an increasing availability of
trajectory data. As a result, researchers devoted their efforts to developing analysis methods …

Human mobility prediction is of great importance for a wide spectrum of location-based
applications. However, predicting mobility is not trivial because of three challenges: 1) the …

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay
between space and time. Several available surveys capture STDM advances and report a …

In this paper, we address the issue of predicting the next location of an individual based on
the observations of his mobility behavior over some period of time and the recent locations …

The analysis of human location histories is currently getting an increasing attention, due to
the widespread usage of geopositioning technologies such as the GPS, and also of online …

Providing location recommendations becomes an important feature for location-based social
networks (LBSNs), since it helps users explore new places and makes LBSNs more …

Understanding human trajectory patterns is an important task in many location based social
networks (LBSNs) applications, such as personalized recommendation and preference …

Trajectory prediction of objects in moving objects databases (MODs) has garnered wide
support in a variety of applications and is gradually becoming an active research area. The …

The design of mobility-aware framework for edge/fog computing for IoT systems with back-
end cloud is gaining research interest. In this paper, a mobility-driven cloud-fog-edge …