Abstract
Internet of Things (IoT) encompasses a huge number of users and physical entities, which usually have temporal and spatial features. User spatiotemporal profile is the key prerequisites to build personalized applications in IoT. Therefor, a user spatiotemporal profile mining algorithm was proposed to discover travel patterns from user Global Positioning System(GPS) trajectories. A GPS trajectory was transformed into a sequence of Regions of Interest (ROI) based on spatial and temporal property of GPS points. Then a pattern-growth mining algorithm was proposed to mine asynchronous periodic sequential patterns with multiple minimum item supports, which were not only occurring frequently, but also appearing periodically. The experimental results showed the efficiency and accuracy of the proposed algorithm.
| Translated title of the contribution | Spatiotemporal user profile mining algorithm in Internet of things |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 2429-2444 |
| Number of pages | 16 |
| Journal | Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS |
| Volume | 26 |
| Issue number | 9 |
| DOIs | |
| State | Published - 1 Sep 2020 |
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