Indexing the Trajectories of Moving Objects in Symbolic Indoor Space

Indoor spaces accommodate large populations of individuals. With appropriate indoor positioning, e.g., Bluetooth and RFID, in place, large amounts of trajectory data result that may serve as a foundation for a wide variety of applications, e.g., space planning, way finding, and security. This scenario calls for the indexing of indoor trajectories. Based on an appropriate notion of indoor trajectory and definitions of pertinent types of queries, the paper proposes two R-tree based structures for indexing object trajectories in symbolic indoor space. The RTR-tree represents a trajectory as a set of line segments in a space spanned by positioning readers and time. The TP²R-tree applies a data transformation that yields a representation of trajectories as points with extension along the time dimension. The paper details the structure, node organization strategies, and query processing algorithms for each index. An empirical performance study suggests that the two indexes are effective, efficient, and robust. The study also elicits the circumstances under which our proposals perform the best.