Name
Title | ||
|---|---|---|
Matching Subscription Over Geo-Textual Streams from IoT via Social-Aware Clustering and Apache Flink |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1142/S0218126621502959 | JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS |
Keywords | DocType | Volume |
Geo-message stream, subscription matching, social, parallel | Journal | 30 |
Issue | ISSN | Citations |
16 | 0218-1266 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohui Huang | 1 | 5 | 3.15 |
| Ze Deng | 2 | 38 | 6.23 |
| Lizhe Wang | 3 | 2973 | 191.46 |
| Tao Liu | 4 | 67 | 22.11 |
| Chengyu Zhang | 5 | 0 | 0.68 |