Optimizing Operators for Temporal and Spatiotemporal Data

Mobile device and IoT applications have become highly available. A common characteristic of these applications is operating on temporal and/or spatiotemporal data that is often in the form of data streams. This paper presents optimizations for the spatiotemporal operators in the core of two important classes of applications, namely, health monitoring and contact tracing. The former requires efficient data streams and/or timeseries correlations for monitoring temporal events, and the latter optimizes temporal aggregation joins for spatiotemporal data (trajectories). The broader contributions of this dissertation are two novel frameworks that offer effective implementations of such applications, demonstrated experimentally with real and synthetic data.