Optimizing Airline Efficiency Using Big Data and Predictive Analytics

Year : 2026 | Volume : 13 | Issue : 01 | Page : 13 18
By

Tamasa Priyadarsini,

Achinta Kumar Palit,

Preetiprada Samantara,

  1. Assistant Professor, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Ghangapatana, Bhubaneswar, Odisha, India
  2. Assistant Professor, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Ghangapatana, Bhubaneswar, Odisha, India
  3. Student, Department of Computer Science and Engineering, Gandhi Institute of Excellent Technocrats, Ghangapatana, Bhubaneswar, Odisha, India

Abstract

Recent technological advancements have resulted in the generation of vast volumes of data across industries, including the airline sector, supporting operational control and service quality. Big data analytics (BDA) enables organizations to analyze large and complex datasets to derive actionable insights that support highly informed decision-making and truly superior operational performance. This review paper systematically analyzes twenty relevant research studies to explore the application of BDA within the airline industry. A structured search was carried out across major scholarly databases, including Web of Science, ScienceDirect, Google Scholar, and IEEE Xplore, using relevant keywords associated with BDA, data mining, predictive analysis, and machine learning. The reviews show that BDA is commonly applied in airline operations, optimizations, customer service, risk management, safety, and aircraft maintenance. The review further reveals that the adoption of BDA in the airline industry is constrained by challenges, such as data integration complexities, strict regulatory compliance requirements, data privacy, and security concerns. In summary, this study presents a comprehensive review of current BDA applications in the airline sector while highlighting key implementation challenges. These insights enrich the existing literature and serve as a useful foundation for future research as well as practical applications of data-driven solutions in airline operations.

Keywords: Airlines industry, big data analytics (BDA), data mining, machine learning, predictive analytics

[This article belongs to Journal of Advanced Database Management & Systems ]

How to cite this article: Tamasa Priyadarsini, Achinta Kumar Palit, Preetiprada Samantara. Optimizing Airline Efficiency Using Big Data and Predictive Analytics. Journal of Advanced Database Management & Systems. 2026; 13(01):13-18.
How to cite this URL: Tamasa Priyadarsini, Achinta Kumar Palit, Preetiprada Samantara. Optimizing Airline Efficiency Using Big Data and Predictive Analytics. Journal of Advanced Database Management & Systems. 2026; 13(01):13-18. Available from: https://journals.stmjournals.com/joadms/article=2026/view=242261

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Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 27/01/2026
Accepted 01/02/2026
Published 20/03/2026
Publication Time 52 Days

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