GLAD: A Grid and Labeling Framework with Scheduling for Conflict-Aware $k$kNN Queries

The intelligent transportation systems, e.g., DiDi and Uber, have served as essential travel tools for customers, which foster plenty of studies for the location-based queries on road network. In particular, given a set O of objects and a query point q on a road network, the k Nearest Neighbor (kNN) query returns the k nearest objects in O with the shortest road network distance to q. In literature, most existing solutions for kNN queries tend to reduce the query time, indexing storage, or throughput of the kNN queries while overlooking the correctness of the queries caused by query-query and update-query conflicts. In our work, we propose a grid-based framework on conflict-aware kNN queries on moving objects which aims to optimize system throughput while guaranteeing query correctness. In particular, we first propose efficient index structures and new query algorithms that significantly improve the throughput. We further present novel scheduling algorithms that aim to avoid conflicts and improve the system throughput. Moreover, we devise approximate solutions that provide a controllable trade-off between the conflict of kNN queries and system throughput. Finally, we propose a cost-based dispatching strategy to assign the kNN results to the corresponding queries. Extensive experiments on real-world data demonstrate the effectiveness and efficiency of our proposed solutions over alternatives.