  In today's society, location-based services are widely used which collect a
huge amount of human trajectories. Analyzing semantic meanings of these
trajectories can benefit numerous real-world applications, such as product
advertisement, friend recommendation, and social behavior analysis. However,
existing works on semantic trajectories are mostly centralized approaches that
are not able to keep up with the rapidly growing trajectory collections. In
this paper, we propose a novel large-scale semantic trajectory analysis
algorithm in Apache Spark. We design a new hash function along with efficient
distributed algorithms that can quickly compute semantic trajectory
similarities and identify communities of people with similar behavior across
the world. The experimental results show that our approach is more than 30
times faster than centralized approaches without sacrificing any accuracy like
other parallel approaches.
