A Spatio-Temporal Series Data Model with Efficient Indexing and Layout for Cloud-Based Trajectory Data Management

Massive trajectory data are continuously generated with the rapid development of location-acquisition devices such as vehicles and smartphones. To provide services for applications such as mobility pattern discovery, how to manage such gigantic trajectory data to support queries in an efficient and cost-effective way becomes vitally important. Due to its low cost, large storage capacity, and reliability, cloud storage such as S3 becomes a new paradigm for storing gigantic data and is used in some general-purpose data management systems to strike a balance between performance and monetary costs. However, few systems exploit the inherent features of cloud storage for trajectory data. In this paper, we propose a novel cloud-based trajectory data management technique, called Springbok, to bridge the gap between massive trajectory data and cloud storage. Springbok is designed to address several key issues related to cloud-based trajectory data management. First, Springbok treats trajectories as first-class citizens using a new spatio-temporal series data model that can benefit insertion, query processing, and storage management in the cloud. Second, a holistic indexing scheme that considers features of trajectory data and cloud storage is designed to facilitate efficient queries. Third, based on performance features and billing models of cloud storage, we design effective data layouts for trajectory data and corresponding data flushing and access policies in a tiered cloud storage architecture for performance improvement and cost reduction. We have implemented a fully functional prototype of Springbok and conducted evaluations using both real-world and synthetic datasets, demonstrating its ability to achieve a good tradeoff between performance and monetary costs. Springbok has been open-sourced for public access.