Collective spatial keyword query (CSKQ) is one of the most useful spatial queries in location-based service systems. Although the availability of large-scale activity trajectories has given us useful knowledge of users’ behavior, existing activity trajectory search methods are unable to support CSKQ queries reasonably. This paper studies effective and efficient CSKQ processing on activity trajectories to cover the gap. Specifically, we first formalize the problem by a trajectory based model that considers the spatial, activity and popularity issues, enabling more rational CSKQ results to be returned. To avoid high I/O cost, a novel hybrid index structure is further proposed to seamlessly integrate multi-domain information, so that inferior trajectories can be pruned during query processing. A novel candidate sub-trajectory search algorithm is also presented to reduce computation overhead by a linear scan on the trajectory. The experimental results on real check-in datasets demonstrate the efficiency and scalability of our proposed solution.

中文翻译：

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活动轨迹的集体空间关键词搜索

集体空间关键字查询（CSKQ）是基于位置的服务系统中最有用的空间查询之一。尽管大规模活动轨迹的可用性为我们提供了有关用户行为的有用知识，但是现有的活动轨迹搜索方法无法合理地支持CSKQ查询。本文研究了对活动轨迹的有效且高效的CSKQ处理，以弥补这一空白。具体来说，我们首先通过基于轨迹的模型来对问题进行形式化，该模型考虑空间，活动和受欢迎程度问题，从而能够返回更合理的CSKQ结果。为了避免较高的I / O成本，进一步提出了一种新颖的混合索引结构来无缝集成多域信息，以便在查询处理期间可以修剪劣质的轨迹。还提出了一种新颖的候选子轨迹搜索算法，以通过在轨迹上进行线性扫描来减少计算开销。在实际签入数据集上的实验结果证明了我们提出的解决方案的效率和可扩展性。