As the popularity of SNS- and GPS-equipped mobile devices rapidly grows, numerous location-based applications have emerged. A common scenario is that a large number of users change location and interests from time to time; e.g., a user watches news, blogs, and videos while moving outside. Many online services have been developed based on continuously querying spatial–keyword objects. For instance, Twitter adjusts advertisements based on the location and the content of the message a user has just tweeted. In this paper, we investigate the case of dynamic spatial–keyword objects whose locations and keywords change over time. We study the problem of continuously tracking top-\(k\) dynamic spatial–keyword objects for a given set of queries. Answering this type of queries benefits many location-aware services such as e-commerce potential customer identification, drone delivery, and self-driving stores. We develop a solution based on a grid index. To deal with the changing locations and keywords of objects, our solution first finds the set of queries whose results are affected by the change and then updates the results of these queries. We propose a series of indexing and query processing techniques to accelerate the two procedures. We also discuss batch processing to cope with the case when multiple objects change locations and keywords in a time interval and top-\(k\) results are reported afterward. Experiments on real and synthetic datasets demonstrate the efficiency of our method and its superiority over alternative solutions.

随着配备SNS和GPS的移动设备的普及迅速增长，涌现了许多基于位置的应用程序。常见的情况是，大量用户会不时更改位置和兴趣。例如，用户在户外移动时观看新闻，博客和视频。基于连续查询空间关键字对象，已经开发了许多在线服务。例如，Twitter根据用户刚刚发布的消息的位置和内容来调整广告。在本文中，我们研究了位置和关键字随时间变化的动态空间关键字对象的情况。我们研究了连续跟踪top- \（k \）的问题给定查询的动态空间关键字对象。回答此类查询对许多位置感知服务有利，例如电子商务潜在客户识别，无人机交付和自动驾驶商店。我们开发基于网格索引的解决方案。为了处理对象的位置和关键字的变化，我们的解决方案首先找到其结果受更改影响的查询集，然后更新这些查询的结果。我们提出了一系列索引和查询处理技术来加速这两个过程。我们还讨论了批处理，以应对多个对象在一个时间间隔和顶部- （（k \）结果报告后。在真实数据集和合成数据集上的实验证明了我们方法的效率及其相对于替代解决方案的优越性。