In the data science world, massive amounts of data need to be processed efficiently as part of a high-volume of data processing. As the input data sets are highly disordered, we need to embed the appropriate algorithm to arrange the data in the required order for SQL queries to process the data quickly. Processing data in billions or trillions of rows has become common use cases. Robust data management strategies are required to handle increasing data volume. The main reason for data growth is use of IoT devices, ERP platforms, Social media apps, e-Commerce platforms, streaming data and AI / ML creates more data for data insights. A delay in few milliseconds for each input data sorting can make a difference of several minutes to hours when the system is processing larger data sets. The data sorting mechanisms are measured by their time complexity with the input element size benchmarking the processing time and resources consumed on a specific system. The data sorting performance can be improved by reducing the number of intensive operations (number of CPU cycles) and memory usage for each process when the data is sorted. “Rapid Data Sorting” provides much more efficiency to the program and thereby helps to improve the overall data processing speed. After extensive research and rigorous testing, the proposal below was formulated.
| Published in | International Journal of Data Science and Analysis (Volume 11, Issue 6) |
| DOI | 10.11648/j.ijdsa.20251106.13 |
| Page(s) | 178-185 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Data Sorting, Algorithms Divide and Conquer, Unordered Data Sets, Array, Time Complexity, Big Data Volume, Iteration Index
| [1] |
International Journal of Computer Applications: A publication of Computer Science, Delaware. 2015.
https://www.ijcaonline.org/archives/volume110/number5/19314-0774/ |
| [2] | National Library of Medicine: Fast continuous streaming sort in big streaming data environment under fixed-size single storage. 2022. |
| [3] | Research Gate: Sorting Algorithms in Focus: A Critical Examination of Sorting Algorithm Performance. 2024. |
| [4] |
Quicksort: Quicksort Algorithm. 2025.
https://www.uvm.edu/~cbcafier/cs2240/content/08_bucket_radix_quick_heap/01_quicksort.html |
| [5] |
Theoretical Computer Science: A new technique for sorting data. 2022.
https://www.sciencedirect.com/science/article/abs/pii/S0304397522000512 |
| [6] |
Scientific Research: Improvement of Counting Sorting Algorithm. 2023.
https://www.scirp.org/journal/paperinformation?paperid=128274 |
| [7] | Princeton: Quicksort Algorithm. 2022. |
| [8] |
Science Direct: Systematic review and exploration of new avenues for sorting algorithm. 2021.
https://www.sciencedirect.com/science/article/pii/S2667096821000355 |
| [9] |
Science Direct: A new approach to Mergesort algorithm: Divide smart and conquer. 2024.
https://www.sciencedirect.com/science/article/abs/pii/S0167739X24001225 |
| [10] |
Mathematical Association of America: Striving for Efficiency in Algorithms (Sorting). 2015.
https://old.maa.org/press/periodicals/convergence/striving-for-efficiency-in-algorithms-sorting |
| [11] |
Research Gate: Novel Hash-Based Radix Sorting Algorithm. 2019.
https://www.researchgate.net/publication/339270637_Novel_Hash-Based_Radix_Sorting_Algorithm |
| [12] | Academia: A comparative Study of Sorting Algorithms Comb, Cocktail and Counting Sorting. 2017. |
| [13] |
International Research Journal of Engineering and Technology: A Comparative Study of Selection Sort and Insertion Sort Algorithms. 2016.
https://studylib.net/doc/26056464/acomparativestudyofselectionsortandinsertionsortalgorithm... |
| [14] |
CSUN: Novel Hash-Based Radix Sorting Algorithm. 2019.
https://www.ecs.csun.edu/~av884538/publication/2019UEMCON2.pdf |
| [15] | AACM Digital Library: Best sorting Algorithm for nearly sorted lists. 1980. |
| [16] | IEEE Xplore: Comparative of Advanced Sorting Algorithms (Quick Sort, Heap Sort, Merge Sort, Intro Sort, Radix Sort) Based on Time and Memory Usage. 2021. |
APA Style
Subramaniam, B. (2025). Rapid Data Sorting Technique with Efficient and Dynamic Approach. International Journal of Data Science and Analysis, 11(6), 178-185. https://doi.org/10.11648/j.ijdsa.20251106.13
ACS Style
Subramaniam, B. Rapid Data Sorting Technique with Efficient and Dynamic Approach. Int. J. Data Sci. Anal. 2025, 11(6), 178-185. doi: 10.11648/j.ijdsa.20251106.13
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Download@article{10.11648/j.ijdsa.20251106.13,
author = {Balaji Subramaniam},
title = {Rapid Data Sorting Technique with Efficient and Dynamic Approach
},
journal = {International Journal of Data Science and Analysis},
volume = {11},
number = {6},
pages = {178-185},
doi = {10.11648/j.ijdsa.20251106.13},
url = {https://doi.org/10.11648/j.ijdsa.20251106.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20251106.13},
abstract = {In the data science world, massive amounts of data need to be processed efficiently as part of a high-volume of data processing. As the input data sets are highly disordered, we need to embed the appropriate algorithm to arrange the data in the required order for SQL queries to process the data quickly. Processing data in billions or trillions of rows has become common use cases. Robust data management strategies are required to handle increasing data volume. The main reason for data growth is use of IoT devices, ERP platforms, Social media apps, e-Commerce platforms, streaming data and AI / ML creates more data for data insights. A delay in few milliseconds for each input data sorting can make a difference of several minutes to hours when the system is processing larger data sets. The data sorting mechanisms are measured by their time complexity with the input element size benchmarking the processing time and resources consumed on a specific system. The data sorting performance can be improved by reducing the number of intensive operations (number of CPU cycles) and memory usage for each process when the data is sorted. “Rapid Data Sorting” provides much more efficiency to the program and thereby helps to improve the overall data processing speed. After extensive research and rigorous testing, the proposal below was formulated.
},
year = {2025}
}
TY - JOUR T1 - Rapid Data Sorting Technique with Efficient and Dynamic Approach AU - Balaji Subramaniam Y1 - 2025/11/12 PY - 2025 N1 - https://doi.org/10.11648/j.ijdsa.20251106.13 DO - 10.11648/j.ijdsa.20251106.13 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 178 EP - 185 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20251106.13 AB - In the data science world, massive amounts of data need to be processed efficiently as part of a high-volume of data processing. As the input data sets are highly disordered, we need to embed the appropriate algorithm to arrange the data in the required order for SQL queries to process the data quickly. Processing data in billions or trillions of rows has become common use cases. Robust data management strategies are required to handle increasing data volume. The main reason for data growth is use of IoT devices, ERP platforms, Social media apps, e-Commerce platforms, streaming data and AI / ML creates more data for data insights. A delay in few milliseconds for each input data sorting can make a difference of several minutes to hours when the system is processing larger data sets. The data sorting mechanisms are measured by their time complexity with the input element size benchmarking the processing time and resources consumed on a specific system. The data sorting performance can be improved by reducing the number of intensive operations (number of CPU cycles) and memory usage for each process when the data is sorted. “Rapid Data Sorting” provides much more efficiency to the program and thereby helps to improve the overall data processing speed. After extensive research and rigorous testing, the proposal below was formulated. VL - 11 IS - 6 ER -
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