DITA: A Distributed In-Memory Trajectory Analytics System

Trajectory analytics can benefit many real-world applications, e.g., frequent trajectory based navigation systems, road planning, car pooling, and transportation optimizations. In this paper, we demonstrate a distributed in-memory trajectory analytics system DITA to support large-scale trajectory data analytics. DITA exhibit three unique features. First, DITA supports threshold-based and KNN-based trajectory similarity search and join operations, as well as range queries (i.e., space and time). Second, DITA is versatile to support most existing similarity functions to cater for different analytic purposes and scenarios. Last, DITA is seamlessly integrated into Spark SQL to support easy-to-use SQL and DataFrame API interfaces. Technically, DITA proposes an effective partitioning method, global index and local index, to address the data locality problem. It also devises cost-based techniques to balance the workload, and develops a filter-verification framework for efficient and scalable search and join.