群智大数据：感知、优选与理解

郭斌; 翟书颖; 於志文; 周兴社

doi:10.11959/j.issn.2096-0271.2017052

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群智大数据：感知、优选与理解

研究 | 更新时间：2024-06-03

- 群智大数据：感知、优选与理解
- Crowdsensing big data:sensing,data selection,and understanding
- 大数据 2017年3卷第5期页码：2017052
- 作者机构：
  
  1. 西北工业大学计算机学院，陕西西安 710129
  2. 西北工业大学明德学院，陕西西安 710129
- 作者简介：
  
  [ "郭斌（1980-），男，博士，西北工业大学计算机学院教授、博士生导师，主要研究方向为普适计算、群智感知计算。" ]
  [ "翟书颖（1981-），女，西北工业大学明德学院讲师，主要研究方向为物联网与大数据分析。" ]
  [ "於志文（1977-），男，博士，西北工业大学计算机学院教授、博士生导师，主要研究方向为普适计算与智能感知。" ]
  [ "周兴社（1955-），男，博士，西北工业大学计算机学院教授、博士生导师，主要研究方向为信息物理融合计算。" ]
- 基金信息：
  
  国家重点基础研究发展计划（“973”计划）基金资助项目;The National Key Basic Research Program of China(973 Program)(2015CB352400);国家自然科学基金资助项目;The National Natural Science Foundation of China(61332005);国家自然科学基金资助项目;The National Natural Science Foundation of China(61373119)
- DOI：10.11959/j.issn.2096-0271.2017052
  中图分类号： TP393
- 网络首发：2017-09，
  
  纸质出版：2017-09-20
- 稿件说明：
移动端阅览
郭斌, 翟书颖, 於志文, 等. 群智大数据：感知、优选与理解[J]. 大数据, 2017,3(5):2017052.

Bin GUO, Shuying ZHAI, Zhiwen YU, et al. Crowdsensing big data:sensing,data selection,and understanding[J]. Big data research, 2017, 3(5): 2017052.
郭斌, 翟书颖, 於志文, 等. 群智大数据：感知、优选与理解[J]. 大数据, 2017,3(5):2017052. DOI： 10.11959/j.issn.2096-0271.2017052.

Bin GUO, Shuying ZHAI, Zhiwen YU, et al. Crowdsensing big data:sensing,data selection,and understanding[J]. Big data research, 2017, 3(5): 2017052. DOI： 10.11959/j.issn.2096-0271.2017052.

摘要

移动群智感知是一种新的大规模感知模式，它利用随身携带的智能移动终端（智能手机、可穿戴设备等）形成大规模、随时随地且与人们日常生活密切相关的感知系统。通过广大用户参与获取的感知数据具有多模态、富内容、时空性、人本性等特征。以群智大数据为目标对象，就其感知、优选和理解等关键问题进行阐述和探讨，并对笔者在该领域的最新研究成果进行介绍。

Abstract

Mobile crowdsensing (MCS) has become an emerging paradigm for large-scale sensing.It empowers ordinary citizens to contribute data sensed or generated from their mobile devices (e.g.

smartphones

wearable devices)

aggregates and fuses the data in the cloud for crowd intelligence extraction and human-centric service delivery.The data contributed by the crowd in MCS systems presents the features such as multi-modal

rich-content

spatio-temporal

and human-centric.The key challenges and techniques about crowdsensing big data were discussed.The recent progress of our group in this promising research area was described.

关键词

Keywords

references

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