Frontiers of Computer Science

, Volume 6, Issue 3, pp 293–312

Linking temporal records

  • Pei Li
  • Xin Luna Dong
  • Andrea Maurino
  • Divesh Srivastava
Research Article

DOI: 10.1007/s11704-012-2002-5

Cite this article as:
Li, P., Dong, X.L., Maurino, A. et al. Front. Comput. Sci. (2012) 6: 293. doi:10.1007/s11704-012-2002-5

Abstract

Many data sets contain temporal records which span a long period of time; each record is associated with a time stamp and describes some aspects of a real-world entity at a particular time (e.g., author information in DBLP). In such cases, we often wish to identify records that describe the same entity over time and so be able to perform interesting longitudinal data analysis. However, existing record linkage techniques ignore temporal information and fall short for temporal data.

This article studies linking temporal records. First, we apply time decay to capture the effect of elapsed time on entity value evolution. Second, instead of comparing each pair of records locally, we propose clustering methods that consider the time order of the records and make global decisions. Experimental results show that our algorithms significantly outperform traditional linkage methods on various temporal data sets.

Keywords

temporal datarecord linkagedata integration

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pei Li
    • 1
  • Xin Luna Dong
    • 2
  • Andrea Maurino
    • 1
  • Divesh Srivastava
    • 2
  1. 1.Department of Informatics, Systems and CommunicationUniversity of Milan-BicoccaMilanItaly
  2. 2.Data Management DepartmentAT&T Labs-ResearchFlorham ParkUSA