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Sorting Smarter: Unveiling Algorithmic Efficiency and User-Friendly Applications
  • Navya Tiwari
Navya Tiwari
Meru international school, Meru international school

Corresponding Author:[email protected]

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This research delves into a comprehensive analysis of sorting algorithms, aiming to shed light on their runtime efficiency, space complexity, parallelizability, and stability. Focusing on five fundamental sorting algorithmsâ\euro”Quicksort, Mergesort, Bubble Sort, Selection Sort, and Insertion Sortâ\euro”the study employs Python implementations validated against various datasets. The efficiency and running time analysis cover diverse scenarios, including pre-sorted, reverse-sorted, and randomly ordered datasets. The time complexity, evaluated for best-case, worst-case, and average-case scenarios, unveils distinct performance characteristics. Space complexity is measured to assess the memory efficiency of each algorithm.
A key aspect of the research is the comparative analysis, exploring how these algorithms perform concerning input sizes and distributions. Notably, the study evaluates the potential speedup through parallel implementations, revealing substantial efficiency gains. Surprisingly, Insertion Sort exhibits outstanding efficiency in parallelization, challenging conventional expectations. The stability analysis classifies Quick Sort as unstable, while Merge Sort, Insertion Sort, Bubble Sort, and Selection Sort demonstrate stability.
The project’s unique contribution lies in the development of a user-friendly web-based application accessible at https://sortdata.w3spaces.com/index.html. This interactive platform translates algorithmic recommendations into a practical user experience. Users dynamically input datasets, receiving real-time suggestions on the most suitable sorting algorithm based on dataset size. The sorted data is visually presented in both tabular and graphical formats, enhancing user comprehension. In conclusion, this research not only contributes valuable insights into algorithmic behaviors but also bridges the gap between theoretical analyses and practical applications, providing a robust foundation for understanding sorting algorithms and their real-world implications.