Mining the Most Influential k-Location Set from Massive Trajectories
文献类型：会议论文
| 作者 | Yuhong Li; Jie Bao; Yanhua Li; Yingcai Wu; Zhiguo Gong; Yu Zheng. |
| 出版日期 | 2016 |
| 会议名称 | ACM SIGSPATIAL 2016 |
| 会议地点 | California, USA |
| 英文摘要 | Mining the most influential k-location set finds k locations, traversed by the maximum number of unique trajectories, in a given spatial region. These influential locations are valuable for resource allocation applications, such as selecting charging stations for electric automobiles and suggesting locations for placing billboards. This problem is NP-hard and usually calls for an interactive mining processes, e.g., changing the spatial region and k, or removing some locations (from the results in the previous round) that are not eligible for an application according to the domain knowledge. Thus, efficiency is the major concern in addressing this problem. In this paper, we propose a system by using greedy heuristics to expedite the mining process. The greedy heuristic is efficient with performance guarantee. We evaluate the performance of our proposed system based on a taxi dataset of Tianjin, and provide a case study on selecting the locations for charging stations in Beijing |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10353]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Yuhong Li,Jie Bao,Yanhua Li,et al. Mining the Most Influential k-Location Set from Massive Trajectories[C]. 见:ACM SIGSPATIAL 2016. California, USA. |
入库方式： OAI收割
来源：深圳先进技术研究院
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