AI-Driven Indexing Strategies
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I2P112Keywords:
AI, Indexing, Machine Learning, Deep Learning, Natural Language Processing, Information Retrieval, Query OptimizationAbstract
Artificial Intelligence (AI) has transformed to manage, retrieve, and organize information within various fields. Indexing, which is a main feature of database and information retrieval systems, has been based on rule based and heuristic indexing. Nevertheless, as the amount and complexity of information grow, such traditional forms of indexing are no longer enough. The AI-based indexing approaches make use of machine learning (ML), natural language processing (NLP) and deep learning algorithms to automatically generate, optimize, and maintain indexes to enhance search efficiency, query performance, and data accessibility. The paper will provide an extensive discussion of AI-based indexing methods with emphasis on recent developments, issues and application in practice. It also examines the effect of AI indexing on query response time, storage optimization, and retrieval accuracy to provide a direction of future research and implementation
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