Online Staypoint Detection in High-Frequency Location Update Streams

The increasing availability of indoor and outdoor location sensors raises the interest for understanding the mobility in different places, e.g., in museums, train stations, or cities. One of the highly used approaches for understanding the mobility of individuals is to detect staypoints from trajectories. Although offline detection of staypoints fits best for long-term planning, some applications require online staypoint detection from trajectories, like online recommendation systems. Latency plays an important role here. In this paper, we propose a stream-based approach for staypoint detection, which can be realized by applying a Data Stream Management System. We claim that the proposed approach can detect staypoints with low latency from the high-frequency location update streams. To evaluate our approach, we compare it with a batch-based approach on real data from an indoor tracking system and Geolife dataset. The results demonstrate that the online approach detects staypoints with much lower latency compared to traditional approaches like offline and batch processing. Moreover, we prove that the accuracy of the stream-based approach is similar to the batch-based approach.