期刊文献+

时空众包数据管理技术研究综述 被引量:54

Survey on Spatiotemporal Crowdsourced Data Management Techniques
下载PDF
导出
摘要 近年来,众包为传统数据管理提供了一种通过汇聚群体智慧求解问题的新模式,并成为当前数据库领域的研究热点之一.特别是随着移动互联网技术与共享经济模式的快速发展,众包技术已融入到各类具有时空数据的应用场景中,例如各类O2O(online-to-offline)应用、实时交通监控与动态物流管理等.简言之,这种应用众包技术处理时空数据的方式称为时空众包数据管理.对近期在时空众包数据管理方面的研究工作进行综述,首先阐述了时空众包的概念,解释了其与传统众包技术的关系,并介绍了各类典型的时空众包应用;随后描述了时空众包应用平台的工作流程及其任务特点;然后讨论了时空众包数据管理的3项核心研究问题和3类应用技术;最后,总结了时空众包数据管理技术的研究现状并展望了其未来潜在的研究方向,为相关研究人员提供了有价值的参考. In recent years, crowdsourcing, which utilizes the intelligence of crowds to solve problems, provides a novel data processing paradigm for traditional data management challenges and has become one of the hottest research topics. In particular, due to the rapid development of mobile Internet and sharing economy, crowdsourcing not only becomes a new approach for data collection, but is also integrated into all kinds of application scenarios especially spatiotemporal data management such as online-to-offline(O2O) applications, real-time traffic monitoring, and logistics management. In this paper, a survey is provided on existing research of spatiotemporal crowdsourcing. First of all, the concept and representative applications of spatiotemporal crowdsourcing is described, and its relationship with traditional crowdsourcing is explained. Then, the workflow of spatiotemporal crowdsourcing is illustrated. Furthermore, three core research problems and three categories of techniques of spatiotemporal crowdsourcing are discussed. Finally, the state-of-the-art studies of spatiotemporal crowdsourcing are summarized and promising future research directions for the research community are presented.
作者 童咏昕 袁野 成雨蓉 陈雷 王国仁 TONG Yong-Xin YUAN Ye CHENG Yu-Rong CHEN Lei WANG Guo-Ren(State Key Laboratory of Software Development Environmnt (Beihang University), Beijing 100191, China School of Computer Science and Engineering, Beihang University, Beijing 100191, China School of Computer Science and Engineering, Northeastern University, Shenyang 110004, China Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China)
出处 《软件学报》 EI CSCD 北大核心 2017年第1期35-58,共24页 Journal of Software
基金 国家重点基础研究发展计划(973)(2014CB340300) 国家自然科学基金(61502021 61622202 61572119 U1401256) 北京航空航天大学软件开发环境国家重点实验室开放课题(SKLSDE-2016ZX-13)~~
关键词 时空众包 共享经济 O2O模式 任务分配 质量控制 隐私保护 spatiotemporal crowdsourcing sharing economy O2O mode task assignment quality control privacy protection
  • 相关文献

参考文献6

二级参考文献233

  • 1潘晓,肖珍,孟小峰.位置隐私研究综述[J].计算机科学与探索,2007,1(3):268-281. 被引量:65
  • 2Yang B, Lu H, Jensen C S. Scalable continuous range monitoring of moving objects in symbolic indoor space//Proeeedings of the 18th ACM Conference on Information and Knowledge Management. Hong Kong, China, 2009:671-680. 被引量:1
  • 3Wolfson O, Sistla P A, Chamberlain S, Yesha Y. Updating and querying databases that track mobile units. Distributed and Parallel Databases, 1999, 7(3): 257-387. 被引量:1
  • 4Pfoser D, Jensen C S. Capturing the uncertainty of movingobjects representations//Proceedings of the 6th International Symposium on Advances in Spatial Databases. Hong Kong, China, 1999:111-132. 被引量:1
  • 5Cheng R: Kalashnikov D V, Prabhakar S. Querying imprecise data in moving object environments. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(9): 1112- 1127. 被引量:1
  • 6Zhang M, Chen S, Jensen C S, Ooi B C, Zhang Z. Effectively indexing uncertain moving objects for predictive queries// Proceedings of the VLDB Endowment. Lyon, 2009, 2 (1): 1198-1209. 被引量:1
  • 7Cheng R, Chen L, Chen J, Xie X. Evaluating probability threshold k-nearest-neighbor queries over uncertain data// Proceedings of the 12th International Con/erence on Extending Database Technology. Saint Petersburg, 2009 :672-683. 被引量:1
  • 8Tao Y, Cheng R, Xiao X, Ngai W K, Kao B, Prabhakar S. Indexing multi-dimensional uncertain data with arbitrary probability density funetions//Proceedings of the 31st International Conference on Very Large Data Bases. Trondheim, 2005 : 922-933. 被引量:1
  • 9Kalashnikov D V, Ma Y, Mehrotra S, Hariharan R. Index for fast retrieval of uncertain spatial point data//Proceedings of the 14th ACM International Symposium on Geographic Information Systems. Arlington, 2006:195-202. 被引量:1
  • 10Chen J, Cheng R. Efficient evaluation of imprecise location- dependent queries//Proceedings of the 23rd International Conference on Data Engineering. Istanbul, 2007:586-595. 被引量:1

共引文献307

同被引文献274

引证文献54

二级引证文献230

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部