Research and application of high-precision indoor location-aware big data

doi:10.11772/j.issn.1001-9081.2016.02.0295

Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (2): 295-300.DOI: 10.11772/j.issn.1001-9081.2016.02.0295

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Research and application of high-precision indoor location-aware big data

DENG Zhongliang, ZHANG Senjie, JIAO Jichao, XU Lianming

School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2015-08-29 Revised:2015-09-15 Online:2016-02-03 Published:2016-02-10

基于高精度室内位置感知的大数据研究与应用

邓中亮, 张森杰, 焦继超, 徐连明

北京邮电大学电子工程学院, 北京 100876

通讯作者: 张森杰(1991-),男,浙江嘉兴人,硕士研究生,主要研究方向:室内定位、大数据。
作者简介:邓中亮(1965-),男,湖南衡阳人,教授,博士,主要研究方向:室内定位;焦继超(1983-),男,山东济宁人,讲师,博士,主要研究方向:室内定位、大数据;徐连明(1980-),男,山东淄博人,博士,主要研究方向:室内定位。
基金资助:
国家863计划项目(2014AA123103,2015AA124103);国家自然科学基金资助项目(61372110)。

Abstract

Abstract: With the development of indoor positioning technology, a large amount of indoor location data and user data for consumer behavior makes the indoor Location Big Data (LBD) research and application possible. High-precision indoor location technology breaks the bottleneck of indoor location data with low accuracy. By clustering the indoor location data and dimension reduction pretreatment, a mining model was set up to extract the characteristics of custom and flow in the indoor shopping area. Then using the associated user consumption behavior to predict the characteristics of consumer behaviors, a collaborative mining method and architecture for large data of indoor location was put forward. Experiments on location dataset of billions of users in an airport and a shopping mall in Xidan were conducted. The results verify the accuracy and feasibility of the mining method based on this architecture of indoor positioning data.

Key words: Location Big Data(LBD), indoor high-precision positioning, characteristic mining, spatio-temporal association, Online to Offline(O2O)business model

摘要： 随着室内定位技术的发展,室内位置数据和用户消费行为数据的大量产生为室内位置大数据(LBD)研究和应用提供了可能。基于高精度室内位置感知,突破了室内定位位置数据不准确的瓶颈。通过对室内位置数据聚类、降维等预处理,建立挖掘模型分析并提取了室内商圈区域的聚散和流动等特性,进一步通过特征关联预测用户的消费等行为,提出了室内位置大数据协同挖掘的方法和架构。在某机场商圈、西单某商场亿级用户位置数据集上进行了有效性实验和应用,通过实测数据对比验证了基于此架构室内定位数据的精准性和挖掘方法的可行性。

关键词: 位置大数据, 室内高精定位, 特征挖掘, 时空关联, O2O商业模型

CLC Number:

TP391

DENG Zhongliang, ZHANG Senjie, JIAO Jichao, XU Lianming. Research and application of high-precision indoor location-aware big data[J]. Journal of Computer Applications, 2016, 36(2): 295-300.

邓中亮, 张森杰, 焦继超, 徐连明. 基于高精度室内位置感知的大数据研究与应用[J]. 计算机应用, 2016, 36(2): 295-300.

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Research and application of high-precision indoor location-aware big data

基于高精度室内位置感知的大数据研究与应用

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