Abstract
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) |
| Pages | 890-901 |
| Number of pages | 12 |
| ISBN (Print) | 9781538674741 |
| DOIs | |
| State | Published - Apr 2019 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: This work is supported in part by grants awarded by National Natural Science Foundation of Chine ( NSFC) (No.61832017, 61836007, 61532018)