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Improving the Run-Time of Space-Efficient n-Gram Data Structures Using Apache Spark.

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This study demonstrates how Apache Spark, a computing framework, enhances data structure efficiency. Utilizing its directed acyclic graph (DAG) and resilient distributed datasets (RDD) significantly reduces space and time consumption for complex computations.

Keywords:
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Area of Science:

  • Computer Science
  • Data Structures
  • Distributed Computing

Background:

  • Efficient information storage is a major computer science challenge.
  • Key factors for efficient data structures include reduced space and time complexity.
  • Apache Spark is a distributed computing framework designed to accelerate data processing.

Purpose of the Study:

  • To explore the use of Apache Spark for constructing space- and time-efficient data structures.
  • To evaluate the performance improvements offered by Apache Spark in data processing.
  • To demonstrate the practical efficacy of Spark in optimizing data structure design.

Main Methods:

  • Leveraging Apache Spark's directed acyclic graph (DAG) for process mapping.
  • Utilizing resilient distributed datasets (RDD) for large in-memory computations.
  • Constructing a space-efficient data structure and comparing its runtime with and without Spark.

Main Results:

  • Apache Spark significantly reduces run-time for data processing tasks.
  • The implemented space-efficient data structure shows marked performance gains when used with Spark.
  • Comparison validates the substantial efficiency improvements offered by the Spark framework.

Conclusions:

  • Apache Spark is an effective tool for developing space- and time-efficient data structures.
  • The framework's features, DAG and RDD, are crucial for achieving computational efficiency.
  • This research validates the practical benefits of using distributed computing frameworks like Spark for data management.