SLIM: Scalable Linkage of Mobility Data

TL;DR

SLIM is a scalable, efficient method for linking mobility data entities using spatio-temporal info, leveraging LSH to significantly improve speed and outperform existing methods in accuracy.

Contribution

The paper introduces SLIM, a novel scalable algorithm that combines mobility-based similarity with LSH for fast, accurate entity linkage across mobility datasets.

Findings

SLIM outperforms existing methods in precision and recall.

LSH approach achieves 2-4 orders of magnitude speedup.

Effective in linking entities in large mobility datasets.

Abstract

We present a scalable solution to link entities across mobility datasets using their spatio-temporal information. This is a fundamental problem in many applications such as linking user identities for security, understanding privacy limitations of location based services, or producing a unified dataset from multiple sources for urban planning. Such integrated datasets are also essential for service providers to optimise their services and improve business intelligence. In this paper, we first propose a mobility based representation and similarity computation for entities. An efficient matching process is then developed to identify the final linked pairs, with an automated mechanism to decide when to stop the linkage. We scale the process with a locality-sensitive hashing (LSH) based approach that significantly reduces candidate pairs for matching. To realize the effectiveness and efficiency of our techniques in practice, we introduce an algorithm called SLIM. In the experimental evaluation, SLIM outperforms the two existing state-of-the-art approaches in terms of precision and recall. Moreover, the LSH-based approach brings two to four orders of magnitude speedup.