Scalable linkage across location enhanced services

In this work, we investigate methods for merging spatio-temporal usage and entity records across two location-enhanced services, even when the datasets are semantically different. To address both effectiveness and efficiency, we study this linkage problem in two parts: model and framework. First we discuss models, including κ-l diversity- a concept we developed to capture both spatial and temporal diversity aspects of the linkage, and probabilistic linkage. Second, we aim to develop a framework that brings efficient computation and parallelization support for both models of linkage.