{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:14:22Z","timestamp":1753884862100,"version":"3.41.2"},"reference-count":32,"publisher":"World Scientific Pub Co Pte Ltd","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2021,12,30]]},"abstract":"<jats:p> 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\u2019 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. <\/jats:p>","DOI":"10.1142\/s0218126621502959","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T06:17:11Z","timestamp":1624515431000},"source":"Crossref","is-referenced-by-count":0,"title":["Matching Subscription Over Geo-Textual Streams from IoT via Social-Aware Clustering and Apache Flink"],"prefix":"10.1142","volume":"30","author":[{"given":"Xiaohui","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan, 430074, P. R. China"},{"name":"Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, P. R. 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