Springbok: Efficient and Cost-Effective Trajectory Data Management in the Cloud

To manage massive trajectory data, we propose a novel cloud-based trajectory data management technique, Springbok, which leverages cloud storage to balance performance and monetary costs. Unlike existing key-value, relational, and time series databases, Springbok natively models trajectories as first-class data objects via a spatio-temporal series data model, enabling efficient insertion, query processing, and cost-aware storage management in cloud environments. It further employs an optimized indexing scheme that accounts for both the characteristics of trajectory data and the properties of cloud storage, enabling efficient query execution. In addition, Springbok adopts a tiered cloud storage architecture with carefully designed data layouts, flushing, access, and compression policies, guided by cloud storage performance characteristics and pricing models, to jointly optimize performance and cost. We implemented a fully functional prototype of Springbok supporting core trajectory queries, deletion, and crash recovery, and evaluated it using both real-world and synthetic datasets. The results show that Springbok achieves comparable or better performance than state-of-the-art systems in most cases while significantly reducing monetary costs, demonstrating its effectiveness in balancing performance and cost.