Spatio-Temporal Linkage over Location Enhanced Services

TL;DR

This paper introduces a scalable method for linking user records across different location-based services by leveraging spatio-temporal data, enhancing data integration and analysis capabilities.

Contribution

We propose a novel linkage model based on $k$-$l$ diversity and develop the ST-Link algorithm that efficiently matches entities across large spatio-temporal datasets.

Findings

ST-Link achieves high accuracy in linking records.

The algorithm scales well to large datasets.

Filtering mechanisms significantly reduce search space.

Abstract

We are witnessing an enormous growth in the volume of data generated by various online services. An important portion of this data contains geographic references, since many of these services are \emph{location-enhanced} and thus produce spatio-temporal records of their usage. We postulate that the spatio-temporal usage records belonging to the same real-world entity can be matched across records from different location-enhanced services. Linking spatio-temporal records enables data analysts and service providers to obtain information that they cannot derive by analyzing only one set of usage records. In this paper, we develop a new \emph{linkage model} that can be used to match entities from two sets of spatio-temporal usage records belonging to two different location-enhanced services. This linkage model is based on the concept of $k$-$l$ \emph{diversity} --- that we developed to capture both spatial and temporal aspects of the linkage. To realize this linkage model in practice, we develop a scalable linking algorithm called \emph{ST-Link}, which makes use of effective spatial and temporal filtering mechanisms that significantly reduce the search space for matching users. Furthermore, \emph{ST-Link} utilizes sequential scan procedures to avoid random disk access and thus scales to large datasets. We evaluated our work with respect to accuracy and performance using several datasets. Experiments show that \emph{ST-Link} is effective in practice for performing spatio-temporal linkage and can scale to large datasets.