Abstract:
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 recor...Show MoreNotes: As originally submitted and published there was an error in this document. The authors subsequently provided the following text: "Funding: European Union Regional Development Fund of the Mazowieckie Voivodeship." The original article PDF remains unchanged.
Metadata
Abstract:
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.
Notes: As originally submitted and published there was an error in this document. The authors subsequently provided the following text: "Funding: European Union Regional Development Fund of the Mazowieckie Voivodeship." The original article PDF remains unchanged.
Date of Conference: 17-20 December 2022
Date Added to IEEE Xplore: 26 January 2023
ISBN Information:
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