Identification of real-world objects in multiple databases

Object identification is an important issue for integration of data from different sources. The identification task is complicated, if no global and consistent identifier is shared by the sources. Then, object identification can only be performed through the identifying information, the objects data provides itself. Unfortunately real-world data is dirty, hence identification mechanisms like natural keys fail mostly - we have to take care of the variations and errors of the data. Consequently, object identification can no more be guaranteed to be fault-free. Several methods tackle the object identification problem, e.g. Record Linkage, or the Sorted Neighborhood Method. Based on a novel object identification framework, we assessed data quality and evaluated different methods on real data. One main result is that scalability is determined by the applied preselection technique and the usage of efficient data structures. As another result we can state that Decision Ree Induction achieves better correctness and is more robust than Record Linkage.