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.

In this demo paper, we present the new module of our HealthDist system that performs contact tracing in a privacy-preserving manner and considers the COVID-19 exposure risk. This is achieved by answering a new spatio-temporal query, dubbed ST-Aggregate Join, which calculates the COVID-19 exposure risk of an individual on their devices. It utilizes a special-purpose access structure to record the trajectories of users on their devices and optimize the ST-Aggregate Join processing. We demonstrate interactively using a smartphone application how our system can provide effective contact tracing within a university campus. We also illustrate how our new module is working through an intuitive web interface that shows the exposure risk of a person by coloring the trajectory of the infected person and the person(s) in high risk in a preloaded real dataset.

The affordable mobile sensing technologies and the rise of spatio-temporal applications granted trajectory data and data streaming high importance. This led to the need for efficient data stream and trajectory-both historical and real-time-data processing. Tremendous effort has been put in this context, especially, optimizing index structures for certain crucial operations, such as trajectory join and trajectory similarity, which are used widely in many monitoring and analytic applications. In this paper, we propose an access structure that optimizes trajectory joins for contact tracing and avoidance applications. Our prelim evaluation shows that our proposed spatio-temporal index potentially can enhance the performance of contact tracing applications that require trajectory join operations.

Traffic congestion has a significant negative impact on the accelerating pace of daily human activities. Traffic jams increase the transportation costs for goods and humans. They are also amongst the leading factors for pollution in the atmosphere and consequently increase health risks for the population. One way to reduce the amount of emissions produced by vehicles in traffic jams is to mitigate traffic congestion and promote the usage of public transportation. In this paper, we propose a solution that establishes on-demand, virtual bus lanes to prioritize public transportation over other traffic and provide detour guidelines for other drivers, while causing insignificant detour penalties. Our solution leverages incremental window aggregations to identify the busiest road segments, priority scheduling, and Dijkstra shortest path algorithm to shape and detour traffic. Our experimental evaluation shows the effectiveness of our Environment Protective Traffic Orchestration (EPTrOn) algorithm in identifying and alleviating traffic congestions.