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One of the major challenges of data integration is to resolve conflicting numerical attribute values caused by data heterogeneity. In addressing this problem, existing approaches proposed in prior literature often ignore such data inconsistencies or resolve them in an ad hoc manner. In this study, we propose a decision-theoretical framework that resolves numerical value conflicts in a systematic manner. The framework takes into consideration the consequences of incorrect numerical values and selects the value that minimizes the expected cost of errors for all data application problems under consideration. Experimental results show that significant savings can be achieved by adopting the proposed framework instead of ad hoc approaches.
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