Automatic User Identification Method across Heterogeneous Mobility Data Sources

With the ubiquity of location based services and applications, large volume of mobility data has been generated routinely, usually from heterogeneous data sources, such as different GPS-embedded devices, mobile apps or location based service providers. In this paper, we investigate efficient ways of identifying users across such heterogeneous data sources. We present a MapReduce-based framework called Automatic User Identification (AUI) which is easy to deploy and can scale to very large data set. Our framework is based on a novel similarity measure called the signal based similarity (SIG) which measures the similarity of users' trajectories gathered from different data sources, typically with very different sampling rates and noise patterns. We conduct extensive experimental evaluations, which show that our framework outperforms the existing methods significantly. Our study on one hand provides an effective approach for the mobility data integration problem on large scale data sets, i.e., combining the mobility data sets from different sources in order to enhance the data quality. On the other hand, our study provides an in-depth investigation for the widely studied human mobility uniqueness problem under heterogeneous data sources.