Geo-textual data that contain spatial, textual, and temporal information are being generated at a very high rate. These geo-textual data cover a wide range of topics. Users may be interested in receiving local popular topics from geo-textual messages. We study the cluster-based subscription matching (CSM) problem. Given a stream of geo-textual messages, we maintain up-to-date clustering results based on a threshold-based online clustering algorithm. Based on the clustering result, we feed subscribers with their preferred geo-textual message clusters according to their specified keywords and location. Moreover, we summarize each cluster by selecting a set of representative messages. The CSM problem considers spatial proximity, textual relevance, and message freshness during the clustering, cluster feeding, and summarization processes. To solve the CSM problem, we propose a novel solution to cluster, feed, and summarize a stream of geo-textual messages efficiently. We evaluate the efficiency of our solution on two real-world datasets and the experimental results demonstrate that our solution is capable of high efficiency compared with baselines.
|Original language||English (US)|
|Title of host publication||2019 IEEE 35th International Conference on Data Engineering (ICDE)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||12|
|State||Published - Apr 2019|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work is supported in part by grants awarded by National Natural Science Foundation of Chine ( NSFC) (No.61832017, 61836007, 61532018)