Location-aware publish/subscribe
Location-aware publish/subscribe
复制标题
DOI：
10.1145/2487575.2487617
复制
发表时间：
2013-08
期刊：
影响因子：
--
通讯作者：
Guoliang Li;Yang Wang;Ting Wang;Jianhua Feng
中科院分区：
文献类型：
--
作者：
Guoliang Li;Yang Wang;Ting Wang;Jianhua Feng
基于位置的服务已经在移动的设备上变得广泛可用。现有的方法采用拉模型或用户发起的模型，其中用户向服务器发出查询，服务器用位置感知的答案进行回复。为了向用户提供即时回复，推模型或服务器启动模型正在成为下一代基于位置的服务中不可避免的计算模型。在推送模型中，订阅者注册空间文本订阅以捕获他们的兴趣，发布者发布空间文本消息。这就需要一个高性能的位置感知的发布/订阅系统提供发布者的消息，以相关subscribers.In本文中，我们解决的研究挑战，出现在设计一个位置感知的发布/订阅系统。我们提出了一个基于rtree的索引结构，将文本描述集成到rtree节点。我们设计有效的过滤算法和开发有效的修剪技术，以提高过滤效率。实验结果表明，我们的方法取得了很高的性能。例如，我们的方法可以在一秒钟内过滤500条tweet，用于商品计算机上的1000万注册订阅。
Location-based services have become widely available on mobile devices. Existing methods employ a pull model or user-initiated model, where a user issues a query to a server which replies with location-aware answers. To provide users with instant replies, a push model or server-initiated model is becoming an inevitable computing model in the next-generation location-based services. In the push model, subscribers register spatio-textual subscriptions to capture their interests, and publishers post spatio-textual messages. This calls for a high-performance location-aware publish/subscribe system to deliver publishers' messages to relevant subscribers.In this paper, we address the research challenges that arise in designing a location-aware publish/subscribe system. We propose an rtree based index structure by integrating textual descriptions into rtree nodes. We devise efficient filtering algorithms and develop effective pruning techniques to improve filtering efficiency. Experimental results show that our method achieves high performance. For example, our method can filter 500 tweets in a second for 10 million registered subscriptions on a commodity computer.