.png){kind=icon}
返回
Developing a streaming data processing workflow for querying space-time activities from geotagged tweets
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compenvurbsys.2015.12.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The critical dimensions in describing space-time activities are what, where, when, and who, which are frequently applied to collect data about basic functions people perform in space in the course of a day. Collecting data about these dimensions using activity-based surveys has presented researchers with a number of technical and social limitations, ranging from the restricted period of time participants have to record their activities to the level of accuracy with which participants complete a survey. This paper, proposes a new streaming data processing workflow for querying space-time activities (STA) as a by-product of microblogging communication. It allows exploring a large volume of geotagged tweets to discover STA patterns of daily life in a systematic manner. A sequence of tasks have been implemented using different cloud-based computing resources for handling over one million of daily geotagged tweets from Canada for a period of six months. The STA patterns have revealed activity choices that might be attributable to personal motivations for communicating an activity in social networks. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Space-time activity
Twitter
Streaming data processing
Cloud computing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.3
论文数:
1.6K
被引数:
8.3K
机构
引用论文
Monitoring and Analyzing Big Traffic Data of a Large-Scale Cellular Network with Hadoop.png)
IEEE NETWORK
IEEE NETWORKIF6.3
PREAI
分享收藏
查看更多内容