AI-Powered Intelligent Search Systems for Big Data Environments

  • Authors

    • Sibusiso Department of Management Studies, Mbabane State University, Eswatini Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2022PII1W2J

    Published 10-04-2022

  • Artificial Intelligence, Big Data, Intelligent Search Systems, Machine Learning, Natural Language Processing, Information Retrieval, Distributed Computing, Semantic Search, Deep Learning

    Issue

    Section

    Articles

    How to Cite

    [1]
    Sibusiso, “AI-Powered Intelligent Search Systems for Big Data Environments”, IJAIDT, vol. 5, no. 2, pp. 01–14, Oct. 2022, doi: 10.67228/30713315/IJAIDT-2022PII1W2J.
  • Abstract

    Contemporary digital ecosystems generate vast and diverse data, making traditional keyword-based search engines insufficient. This paper analyzes intelligent AI-based search systems in big data environments, highlighting developments up to 2018. It explains how techniques like machine learning, natural language processing, deep learning, and semantic reasoning enhance search accuracy, scalability, and contextual understanding. These systems use learning-based ranking, user behavior analysis, and adaptive indexing to deliver personalized and efficient results, while handling unstructured data and dynamic updates. The study discusses system components such as data ingestion, preprocessing, indexing, query understanding, ranking, and feedback mechanisms. It reviews key advancements in semantic search, learning-to-rank, and distributed search systems. Performance evaluation using metrics like precision, recall, F1-score, and latency shows significant improvement over traditional methods. The findings confirm that AI-based search systems provide more accurate and user-satisfying results, especially for large-scale unstructured data. Challenges such as data privacy, computational complexity, and interpretability are also addressed, along with future directions like reinforcement learning and real-time adaptive systems.

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