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A novel continuous streaming sorting algorithm addresses memory overflow in big data environments. This fast sorting method significantly outperforms existing algorithms for large datasets, achieving 100% accuracy with optimized storage.

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Area of Science:

  • Computer Science
  • Data Engineering
  • Algorithm Analysis

Background:

  • Big streaming data environments present challenges due to continuous data flow, often leading to memory overflow issues.
  • Classic sorting algorithms require entire datasets to be stored, which is infeasible when data size (n) significantly exceeds storage capacity (M).

Purpose of the Study:

  • To propose a new, fast continuous streaming sorting algorithm designed to overcome storage overflow limitations.
  • To evaluate the performance and accuracy of the proposed algorithm against various datasets and storage constraints.

Main Methods:

  • Development of a novel continuous streaming sorting algorithm.
  • Empirical testing on real-world datasets ranging from 10,000 to 17,000,000 numbers.
  • Comparison of the algorithm's sorting time and accuracy against established sorting algorithms (bubble sort, quick sort, insertion sort, merge sort, external merge sort, streaming data sort) under varying storage sizes (0.01n to 0.50n).

Main Results:

  • The proposed algorithm achieves 100% sorting accuracy with a feasible lower bound storage size of 0.35n.
  • It demonstrates superior sorting time compared to bubble sort, quick sort, insertion sort, and merge sort for datasets larger than 1,000,000 numbers.
  • The algorithm's sorting time is significantly faster: 1,452 times less than external merge sort and 28.1767 times less than streaming data sort.

Conclusions:

  • The developed continuous streaming sorting algorithm effectively handles memory overflow constraints in big data processing.
  • With O(n) time complexity and O(M) space complexity, it offers a highly efficient solution for large-scale streaming data sorting.