Lamps: Location-Aware Moving Top-k Pub/Sub (Extended abstract)

Nishio S., Amagata D., Hara T.. Lamps: Location-Aware Moving Top-k Pub/Sub (Extended abstract). Proceedings - International Conference on Data Engineering 2023-April, 3809 (2023); https://doi.org/10.1109/ICDE55515.2023.00331.We propose a novel system, called Lamps (Location-Aware Moving Top-k Pub/Sub), which continuously monitors the top-k most relevant spatio-textual objects for a large number of moving top-k spatio-textual subscriptions simultaneously. Lamps employs the concept of a safe region to monitor top-k results. However, unlike with existing works that assume static objects, top-k result updates may be triggered by newly generated objects. To continuously monitor the top-k results for massive moving subscriptions efficiently, we propose SQ-tree, a novel index based on safe regions, to filter subscriptions whose top-k results do not change. Moreover, to reduce the expensive cost of safe region re-evaluation, we develop a novel approximation technique for safe region construction. Our experimental results on real datasets show that Lamps achieves higher performance than baseline approaches