Large Scale Windowed Matching

Missing or invalid records in sales data are a common obstacle that can damage the overall effectiveness of market analysis. Completing the data on the basis of the records obtained so far can be formulated in means of a schema matching task. In this paper we present a machine learning based method for performing schema matching for transactional data. The analysis is based on a dataset of over 700.000 transactions from retail stores. We confront the proposed solution with manual and conventional approaches.