Short Paper: Predictive Trajectory Stream Indexing

Due to the widespread availability of GPS devices, a large amount of trajectory data can be collected. These data come in the form of unbounded streams, called trajectory streams. Many applications like urban planning, transportation, and ecology benefit from performing spatial queries on these data, but due to the Big Data size, spatial indexes need to be built to process queries efficiently. Existing systems for trajectory stream data management and analytics do not exploit the online, continuous behavior of trajectory streams for indexing, leading to wasted time. In this paper, we introduce PIMMLI, a Spark-based trajectory stream query processing algorithm that avoids re-indexing spatial data with the arrival of each new batch of points by forecasting the trajectories, expanding their Minimum Bounding Rectangles accordingly. Our experiments on a real-life dataset show that PIMMLI has comparable query execution time with competitors while having up to 29% savings in indexing time.