面向海量低质手机轨迹数据的重要位置发现

重要位置是指人们在日常生活中的主要活动地点,比如居住地和工作地.智能手机的不断发展与普及为人们的日常生活带来了极大的便利.除了通话、上网等传统应用之外,手机连接基站自动生成的日志记录也是用于用户行为模式挖掘的重要数据来源,例如重要位置发现.然而,相关工作面临着诸多挑战,包括轨迹数据规模庞大、位置精度低以及手机用户的多样性.为此,本文提出了一个通用解决框架以提高轨迹数据可用性.该框架包含一个基于状态的过滤模块,提高了数据的可用性,以及一个重要位置挖掘模块.基于此框架设计了两种分布式挖掘算法:GPMA(Grid-based Parallel Mining Algorithm)和SPMA(Station-based Parallel Mining Algorithm).进一步地,为提高挖掘结果的准确性和精确度,从三个方面进行优化:1）使用多元数据的融合技术,提高结果的准确性;2）提出了无工作地人群的发现算法;3）提出了夜间工作人群的发现算法.理论分析和实验结果表明本文算法具有较高的执行效率、可扩展性,并具有更高的精度.

Discovering Important Locations From Massive and Low-Quality Cell Phone Trajectory Data

Important locations mainly refer to the places where people spend much time in the daily life, including their home and working places. The development and popularization of the smart cell phones bring great convenience to our daily life. Besides making calls and surfing the Internet, the logs generated when visiting the base stations also contribute to users' pattern mining, such as important location discovery. However, it's challenging to deal with such kind of trajectory data, due to huge volume, data inaccuracy and diversity of cell phone users. Consequently, a general framework is proposed to improve the usability of trajectory data in this paper, including a filter to improve data usability and a model to get the mining results. Note that two concrete strategies,namely GPMA (Grid-based Parallel Mining Algorithm) and SPMA (Station-based Parallel Mining Algorithm), can be embedded into this framework separately Moreover, some optimization techniques are developed for higher performance, including (i) data fusion techniques, (ii) an algorithm to find users who have no work places, and (iii) an algorithm to find people who work on night and fix their important locations. Theoretical analysis and extensive experimental results on real datasets show that our proposed algorithms are efficient, scalable, and effective.