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Caching Techniques for Flight Delays Prediction in Big Data Using SparkR | IEEE Conference Publication | IEEE Xplore

Caching Techniques for Flight Delays Prediction in Big Data Using SparkR


Abstract:

Over the past years, Flight delays had negative effects on passengers, airlines, and airports. Now it is possible to predict that a flight will be delayed based on the st...Show More

Abstract:

Over the past years, Flight delays had negative effects on passengers, airlines, and airports. Now it is possible to predict that a flight will be delayed based on the statistics of past flights. In this work, a new Dynamic Prediction of the Departure and Arrival Flight Delays (DPDAD) model is created to get the prediction and the probability of delay status in origin and destination airports using certain airline through a website even before booking an airline ticket and suggesting the top ten carriers for the same flight. Most of the previous studies focused on flights departure delay only or arrival delay only. This work focused on both delays at the same time which helps a user to take a better decision. Spark is used as an ecosystem cluster over a Hadoop cluster; it is handled through a SparkR library from R. Most of related works examined the importance of caching in Apache Spark but this work examined Caching storage Techniques and detected that best caching storage depends on ML technique and how it accesses the data when it is overloaded.
Date of Conference: 30 October 2018 - 01 November 2018
Date Added to IEEE Xplore: 04 August 2021
ISBN Information:
Conference Location: Alexandria, Egypt

References

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