Lamps: Location-Aware Moving Top-k Pub/Sub

Huge amounts of spatio-textual objects, such as geo-tagged tweets, are being generated at an unprecedented scale, leading to a variety of applications such as location-based recommendation and sponsored search. Many of these applications need to support moving top-k spatio-textual subscriptions. For example, while walking, a tourist issues a moving subscription and looks for top-k advertisements published by nearby shops. Unfortunately, existing methods that monitor the results of spatio-textual subscriptions support only static top-k subscriptions or moving boolean subscriptions. In this article, 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. To the best of our knowledge, this is the first study of a location-aware moving top-k pub/sub system. As with existing works on continuous moving top-k subscription processing, 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.