基于时空相关性的多签到数据匹配算法

doi:10.11896/j.issn.1002-137X.2018.01.033

计算机科学 ›› 2018, Vol. 45 ›› Issue (1): 188-195.doi: 10.11896/j.issn.1002-137X.2018.01.033

基于时空相关性的多签到数据匹配算法

张晨,李志,朱红松,孙利民

物联网信息安全技术北京市重点实验室北京100093中国科学院信息工程研究所北京100093中国科学院大学网络空间安全学院北京100049,物联网信息安全技术北京市重点实验室北京100093中国科学院信息工程研究所北京100093中国科学院大学网络空间安全学院北京100049,物联网信息安全技术北京市重点实验室北京100093中国科学院信息工程研究所北京100093中国科学院大学网络空间安全学院北京100049,物联网信息安全技术北京市重点实验室北京100093中国科学院信息工程研究所北京100093中国科学院大学网络空间安全学院北京100049

出版日期:2018-01-15 发布日期:2018-11-13
基金资助:
本文受重点研发计划项目(2016YFC1202204),国家自然科学基金面上项目,北京市科委项目(Z161100002616032)(61572231)资助

MIMA:A Multi-identification Check-in Data Matching Algorithm Based on Spatial and Temporal Relations

ZHANG Chen, LI Zhi, ZHU Hong-song and SUN Li-min

Online:2018-01-15 Published:2018-11-13

摘要/Abstract

摘要： 智能产品往往具有标识其唯一性的标签,如公交卡编号、Wi-Fi设备MAC地址等,设备标签以及其使用的时间、地点信息构成了代表人们离散轨迹的签到数据。研究人员针对单种签到数据开展了多方面的研究,但单种签到数据通常比较稀疏,其适应性和性能等受到限制。为此,研究了新的多签到数据问题,提出了一种基于多签到数据的标签匹配算法MIMA,丰富了签到数据,提高了应用性能。该算法首先基于单人多签到数据具有的时空相关性,通过计算多个标签之间的正负关系构建面向多标签的符号网络；在此基础上,摒弃了不适用于签到数据符号网络的分割条件,并通过增加权值分布密度来改进已有FEC(Finding and Extracting Communities from singed social networks)社区发现算法的分割机制,以适应签到数据符号网络的特性,实现多标签的划分。模拟仿真和真实数据的实验均显示MIMA算法具有较好的时间复杂效率和精度。

关键词: 多签到数据,符号网络,社区发现,匹配算法

Abstract: Intelligent product often has a tag which can represent its uniqueness,such as the serial number of bus cards,the MAC address of Wi-Fi devices. The check-in data,which represent people’s discrete trajectory, consist of the tag,the time and the location used by the product.Researchers have made lots of surveys about single kind of check-in data.However,single kind of check-in data are sparse,so the adaptability and performance of the surveys are limited.This paper studied a new problem about multiple kinds of check-in data and proposed an algorithm called MIMA based on multiple kind sof check-in data to enrich check-in data and improve the performance of the surveys.Firstly,MIMA builds up the signed network through calculating the positive and negative values between tags based on the temporal and spatial relations of multiple kinds of check-in data produced by one individu al(1)Then the FEC(Finding and Extracting Communities from singed social networks) community detection algorithm is improved by deleting a cut criteria and considering the weight density to adapt to the specialty of check-in data signed network,and it achieves the goal of partitioning multiple tags which belong to one individual.The effectiveness and efficacy of the proposed algorithm are demons-trated through a set of experiments involving both real and simulated situations.

Key words: Multi-identification check-in data,Singed network,Community detection,Matching algorithm

张晨,李志,朱红松,孙利民. 基于时空相关性的多签到数据匹配算法[J]. 计算机科学, 2018, 45(1): 188-195. https://doi.org/10.11896/j.issn.1002-137X.2018.01.033

ZHANG Chen, LI Zhi, ZHU Hong-song and SUN Li-min. MIMA:A Multi-identification Check-in Data Matching Algorithm Based on Spatial and Temporal Relations[J]. Computer Science, 2018, 45(1): 188-195. https://doi.org/10.11896/j.issn.1002-137X.2018.01.033

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基于时空相关性的多签到数据匹配算法

MIMA:A Multi-identification Check-in Data Matching Algorithm Based on Spatial and Temporal Relations

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