Metadata Gets a Makeover: The Machine Learning Approach
DOI:
https://doi.org/10.15680/IJCTECE.2020.0306001Keywords:
Metadata, Machine Learning, Natural Language Processing, Metadata Management, Data Categorization, Automation, Classification, Data ScalabilityAbstract
Metadata, the data that provides information about other data, has traditionally been managed through manual processes, which often lead to inconsistencies and inefficiencies. With the rapid growth of data and the increasing complexity of digital ecosystems, the need for more sophisticated methods of metadata management has never been greater. Machine Learning (ML) presents a promising solution by automating metadata generation, categorization, and maintenance. This paper explores how ML techniques, including natural language processing (NLP), clustering, and classification algorithms, are transforming metadata management. By reviewing case studies and applying these techniques to real-world datasets, this paper highlights the impact of ML on improving metadata accuracy, scalability, and adaptability in dynamic data environments.
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