Current location-based services (LBS) continuously generate a massive amount of geo-message streams. The cluster-based subscription matching method is an effective means to feed subscribers with related geo-messages from geo-message streaming. However, current cluster-based subscription matching methods only consider the spatial relationship and textual relationship and ignore users’ social relationship. As a result, the matching results may not completely satisfy the requirements of users. In this paper, we proposed a social-aware subscription matching method by taking spatial, textual, and social factors into consideration. Then, we used a cache strategy and a Flink-based acceleration process to reduce the extra time overhead caused by computing the social relationships. A set of extensive experiments have been conducted on a real dataset. The experimental results indicate that our method improves the recall of matching results. Besides, the Flink-based acceleration process with caching can speed up the subscription matching process by a ratio of up to 3.299 compared with the state-of-the-art.

中文翻译：

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通过社交感知集群和 Apache Flink 匹配来自物联网的地理文本流订阅

当前的基于位置的服务 (LBS) 不断生成大量的地理消息流。基于集群的订阅匹配方法是向订阅者提供来自地理消息流的相关地理消息的有效手段。然而，目前基于集群的订阅匹配方法只考虑空间关系和文本关系，而忽略了用户的社交关系。因此，匹配结果可能无法完全满足用户的需求。在本文中，我们提出了一种考虑空间、文本和社会因素的社会感知订阅匹配方法。然后，我们使用缓存策略和基于 Flink 的加速过程来减少计算社交关系带来的额外时间开销。已经在真实数据集上进行了一组广泛的实验。实验结果表明，我们的方法提高了匹配结果的召回率。此外，与现有技术相比，基于 Flink 的缓存加速过程可以将订阅匹配过程加速高达 3.299 倍。