Abstract
We consider data fusion in the case of missing object identification. As a simple example think of fusion of partial overlapping address files of customers extracted from autonomous sites or of an administrative record census. The first example is related to customer relationship management (CRM), while the last one is a substitute of a regular census. This kind of data fusion causes problems of (schema) integration, solving semantic conflicts, and object identification if global identifiers are not locally available and local heterogeneous, autonomous databases are to be accessed. The complexity of the problem is increased by the existence of errors like input or loading errors, mispellings, missing values, and, of course, duplicated entries. We develop a unified framework for such kind of data fusion. We cover the feature selection problem, and embed the data fusion problem into a supervised classification problem. For each pair of records we have to decide whether a definite decision upon matching or not is possible and if it is possible, whether the two records are linked to an identical unit (customer, citizen etc.) or not. Candidates for classification can be selected from likelihood ratio tests (record linkage), classification trees, non linear classification or state vector machines. We illustrate our approach by a running example.
Part of this work was supported by the Berlin-Brandenburg Graduate School in Distributed Information Systems (DFG grant no. GRK 316)
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Lenz, HJ., Neiling, M. (2001). Classification and Fusion. In: Della Riccia, G., Lenz, HJ., Kruse, R. (eds) Data Fusion and Perception. International Centre for Mechanical Sciences, vol 431. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2580-9_6
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DOI: https://doi.org/10.1007/978-3-7091-2580-9_6
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