An efficient data processing framework for mining the massive trajectory of moving objects
文献类型：期刊论文
| 作者 | Zhou, Yuanchun1; Zhang, Yang1; Ge, Yong2; Xue, Zhenghua1; Fu, Yanjie3; Guo, Danhuai1; Shao, Jing1; Zhu, Tiangang1; Wang, Xuezhi1; Li, Jianhui1 |
| 刊名 | Computers environment and urban systems
 |
| 出版日期 | 2017 |
| 卷号 | 61页码:129-140 |
| 关键词 | Big data Trajectory of moving object Compression contribution model Parallel linear referencing Two step consistent hashing |
| ISSN号 | 0198-9715 |
| DOI | 10.1016/j.compenvurbsys.2015.03.004 |
| 通讯作者 | Li, jianhui(lijh@cnic.cn) |
| 英文摘要 | Recently, there has been increasing development of positioning technology, which enables us to collect large scale trajectory data for moving objects. efficient processing and analysis of massive trajectory data has thus become an emerging and challenging task for both researchers and practitioners. therefore, in this paper, we propose an efficient data processing framework for mining massive trajectory data. this framework includes three modules: (1) a data distribution module, (2) a data transformation module, and (3) a high performance i/o module. specifically, we first design a two-step consistent hashing algorithm, which takes into account load balancing, data locality, and scalability, for a data distribution module. in the data transformation module, we present a parallel strategy of a linear referencing algorithm with reduced subtask coupling, easy-implemented parallelization, and low communication cost. moreover, we propose a compression-aware i/o module to improve the processing efficiency. finally, we conduct a comprehensive performance evaluation on a synthetic dataset (1.114 tb) and a real world taxi gps dataset (578 gb). the experimental results demonstrate the advantages of our proposed framework. (c) 2015 elsevier ltd. all rights reserved. |
| WOS研究方向 | Computer Science ; Engineering ; Environmental Sciences & Ecology ; Geography ; Operations Research & Management Science |
| WOS类目 | Computer Science, Interdisciplinary Applications ; Engineering, Environmental ; Environmental Studies ; Geography ; Operations Research & Management Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000390830200003 |
| 出版者 | ELSEVIER SCI LTD |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2374195 |
| 专题 | 计算机网络信息中心 |
| 通讯作者 | Li, Jianhui |
| 作者单位 | 1.Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China 2.Univ N Carolina, Dept Comp Sci, Charlotte, NC USA 3.Rutgers State Univ, MSIS Dept, Piscataway, NJ 08855 USA |
| 推荐引用方式 GB/T 7714 | Zhou, Yuanchun,Zhang, Yang,Ge, Yong,et al. An efficient data processing framework for mining the massive trajectory of moving objects[J]. Computers environment and urban systems,2017,61:129-140. |
| APA | Zhou, Yuanchun.,Zhang, Yang.,Ge, Yong.,Xue, Zhenghua.,Fu, Yanjie.,...&Li, Jianhui.(2017).An efficient data processing framework for mining the massive trajectory of moving objects.Computers environment and urban systems,61,129-140. |
| MLA | Zhou, Yuanchun,et al."An efficient data processing framework for mining the massive trajectory of moving objects".Computers environment and urban systems 61(2017):129-140. |
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来源：计算机网络信息中心
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