Tracking entities in the dynamic world

Abstract

Identifying records referring to the same real world entity over time enables longitudinal data analysis. However, difficulties arise from the dynamic nature of the world: the entities described by a temporal data set often evolve their states over time. While the state of the art approach to temporal entity matching achieves high accuracy, this approach is computationally expensive and cannot handle large data sets. In this paper, we present an approach that achieves equivalent matching accuracy but takes far less time. Our key insight is "static first, dynamic second." Our approach first runs an evidence-collection pass, grouping records without considering the possibility of entity evolution, as if the world were "static." Then, it merges clusters from the initial grouping by determining whether an entity might evolve from the state described in one cluster to the state described in another cluster. This intuitively reduces a difficult problem, record matching with evolution, to two simpler problems: record matching without evolution, then "evolution detection" among the resulting clusters. Experimental results on several temporal data sets show that our approach provides an order of magnitude improvement in run time over the state-of-the-art approach while producing equivalent matching accuracy.