ArticleOpen Access http://dx.doi.org/10.26855/acc.2026.06.008
Research on Distributed Data Cleaning and Standardized Mapping for Heterogeneous Logistics Data
Haoran Xin
College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA.
*Corresponding author: Haoran Xin
Published: June 30, 2026
Abstract
Reliable and interoperable logistics data are essential for real-time supply chain visibility and operational resilience in large-scale fulfillment networks. However, heterogeneous data generated from orders, warehouses, transportation execution, vehicle trajectories, node scanning, and cost settlement often contain inconsistent field definitions, fragmented object relationships, delayed updates, and incompatible coding standards, reducing the reliability of scheduling, warehouse-distribution coordination, cost accounting, and supply chain monitoring. To address this problem, this paper proposes a distributed data cleaning and standardized mapping framework for heterogeneous logistics data. The frame-work partitions high-volume multi-source data into parallel processing units by time windows, regions, vehicles, warehouses, and settlement cycles, and applies format validation, anomaly identification, semantic tagging, master data align-ment, code conversion, and quality verification across distributed processing nodes. Comparative experiments on 30 days of business data from an anonymous third-party logistics platform show that the proposed framework outperforms centralized rule-based processing in field completeness, duplicate reduction, status code matching, trajectory breakpoint repair, order-waybill association, cost standardization, and batch processing efficiency. The results demonstrate that distributed data processing and semantic data integration can provide a scalable data foundation for real-time transportation scheduling, warehouse-distribution coordination, cost reconciliation, supply chain visibility, and operational resilience in complex logistics networks.
Keyword
Heterogeneous logistics data; distributed data processing; standardized mapping; semantic alignment; supply chain visibility
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Copyright
© 2026 by the author(s).
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How to cite this paper
Research on Distributed Data Cleaning and Standardized Mapping for Heterogeneous Logistics Data
How to cite this paper: Haoran Xin. (2026) Research on Distributed Data Cleaning and Standardized Mapping for Heterogeneous Logistics Data. Advances in Computer and Communication, 7(2), 101-105.
DOI: http://dx.doi.org/10.26855/acc.2026.06.008