AI DRIVEN DATA ENRICHMENT PIPELINES IN ENTERPRISE SHIPPING AND LOGISTICS SYSTEM
Keywords:
Data enrichment pipelines, shipping, logistics, Automatic Identification System (AIS), predictive supply chain management.Abstract
This paper examines AI-driven data enrichment pipelines as an engineering andmethodological response to the heterogeneity, incompleteness and semantic fragmentation ofdata in contemporary shipping and logistics enterprises. Enrichment is defined as thesystematic transformation of raw telemetry
References
Bao, K., Bi, J., Gao, M., Sun, Y., Zhang, X., & Zhang, W. (2022). An improved ship trajectory prediction based on AIS data using MHA-BiGRU. Journal of Marine Science and Engineering, 10(6), 804. https://www.mdpi.com/2077-1312/10/6/804
Cheney, J., Chiticariu, L., & Tan, W. C. (2009). Provenance in databases: Why, how, and where. Foundations and Trends® in Databases, 1(4), 379–474. https://www.nowpublishers.com/article/DownloadSummary/DBS-006
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Published
2023-11-15
How to Cite
SUKESH REDDY KOTHA. (2023). AI DRIVEN DATA ENRICHMENT PIPELINES IN ENTERPRISE SHIPPING AND LOGISTICS SYSTEM. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1590–1604. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3486
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