Predicting the next location change and time of change for mobile phone users

M. Ozer, Ilkcan Keles, I. H. Toroslu, P. Senkul, S. Ergüt
{"title":"Predicting the next location change and time of change for mobile phone users","authors":"M. Ozer, Ilkcan Keles, I. H. Toroslu, P. Senkul, S. Ergüt","doi":"10.1145/2675316.2675318","DOIUrl":null,"url":null,"abstract":"Predicting the next location of people from their mobile phone logs has become an active research area. Due to two main reasons this problem is very challenging: the log data is very large and there are variety of granularity levels for specifying the spatial and the temporal attributes. In this work, we focus on predicting the next location change of the user and when this change occurs. Our method has two steps, namely clustering the spatial data into larger regions and grouping temporal data into time intervals to get higher granularity levels, and then, applying sequential pattern mining technique to extract frequent movement patterns to predict the change of the region of the user and its time frame. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results.","PeriodicalId":229456,"journal":{"name":"International Workshop on Mobile Geographic Information Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on Mobile Geographic Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2675316.2675318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

Predicting the next location of people from their mobile phone logs has become an active research area. Due to two main reasons this problem is very challenging: the log data is very large and there are variety of granularity levels for specifying the spatial and the temporal attributes. In this work, we focus on predicting the next location change of the user and when this change occurs. Our method has two steps, namely clustering the spatial data into larger regions and grouping temporal data into time intervals to get higher granularity levels, and then, applying sequential pattern mining technique to extract frequent movement patterns to predict the change of the region of the user and its time frame. We have validated our results with real data obtained from one of the largest mobile phone operators in Turkey. Our results are very encouraging, and we have obtained very high accuracy results.
为手机用户预测下一个地点的变化和变化的时间
通过手机记录预测人们的下一个位置已经成为一个活跃的研究领域。由于两个主要原因,这个问题非常具有挑战性:日志数据非常大,并且用于指定空间和时间属性的粒度级别多种多样。在这项工作中,我们专注于预测用户的下一次位置变化以及这种变化发生的时间。该方法分为两个步骤,首先将空间数据聚类成更大的区域,然后将时间数据分组成更高的时间间隔,从而获得更高的粒度级别,然后利用顺序模式挖掘技术提取频繁的运动模式,从而预测用户所在区域及其时间框架的变化。我们用从土耳其最大的移动电话运营商之一获得的真实数据验证了我们的结果。我们的结果是非常令人鼓舞的,我们获得了非常高的精度结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信