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Plagiarism Checker Data Indexing Technology for Indian Regional Language

Prashanth Kumar H.M.1 , Subramanya Bhat S.2

  1. College of Computer Science, Srinivas University, Mangalore, India.
  2. College of Computer Science, Srinivas University, Mangalore, India.

Section:Case Study, Product Type: Journal Paper
Volume-11 , Issue-4 , Page no. 61-62, Apr-2023

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v11i4.6162

Online published on Apr 30, 2023

Copyright © Prashanth Kumar H.M., Subramanya Bhat S. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Prashanth Kumar H.M., Subramanya Bhat S., “Plagiarism Checker Data Indexing Technology for Indian Regional Language,” International Journal of Computer Sciences and Engineering, Vol.11, Issue.4, pp.61-62, 2023.

MLA Style Citation: Prashanth Kumar H.M., Subramanya Bhat S. "Plagiarism Checker Data Indexing Technology for Indian Regional Language." International Journal of Computer Sciences and Engineering 11.4 (2023): 61-62.

APA Style Citation: Prashanth Kumar H.M., Subramanya Bhat S., (2023). Plagiarism Checker Data Indexing Technology for Indian Regional Language. International Journal of Computer Sciences and Engineering, 11(4), 61-62.

BibTex Style Citation:
@article{H.M._2023,
author = {Prashanth Kumar H.M., Subramanya Bhat S.},
title = {Plagiarism Checker Data Indexing Technology for Indian Regional Language},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2023},
volume = {11},
Issue = {4},
month = {4},
year = {2023},
issn = {2347-2693},
pages = {61-62},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5562},
doi = {https://doi.org/10.26438/ijcse/v11i4.6162}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v11i4.6162}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5562
TI - Plagiarism Checker Data Indexing Technology for Indian Regional Language
T2 - International Journal of Computer Sciences and Engineering
AU - Prashanth Kumar H.M., Subramanya Bhat S.
PY - 2023
DA - 2023/04/30
PB - IJCSE, Indore, INDIA
SP - 61-62
IS - 4
VL - 11
SN - 2347-2693
ER -

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Abstract

Plagiarism is considered a serious academic and ethical offense, as it undermines the values of originality, honesty, and integrity in academic and creative work. India has a diverse linguistic landscape, with over 22 official languages and many more regional languages spoken across the country. Several Indian states have taken steps to promote regional language education in recent years. In this case study we are exposing a very accurate plagiarism checker for all indian regional languages. We are facing many challenges to develop this sort of software. So, mainly the data indexing methods are very interesting in this case. Here we are exposing how data indexing methodology works using ‘Taylor series’ formula in cloud-based storage for Indian regional languages.

Key-Words / Index Term

Indexing, Encryption, Data Sequence, Search Key.

References

[1] J. Li, Z. Xu, Y. Jiang, and R. Zhang, “The overview of big data storage and management. cognitive informatics cognitive computing” in IEEE 13th International Conference on, (pp. 510-513, 2014.
[2] C. Liu, Zhang, C. Yang, D. Georgakopoulos, and J. Chen, “Public auditing for big data storage indexing -,” in A Survey. Computational Science and Engineering (CSE), 2013 IEEE 17th International Conference on, (pp. 1121-1135)., 2015, Dec.
[3] H. Tan, W. Luo, and L. M. Ni, “Taylor series and its functions in data analytics.,” in Proceedings of the22nd ACM International Conference on Information and Knowledge Management (pp. 2149-2183). New York, NY, USA: ACM., 2012.
[4] W. Zhou, C. Yuan, R. Gu, and Y. Huang, “Large scale nearest neighbors search based on neighborhood graph,” in Advanced Cloud and Big Data (CBD), 2013 International Conference on, pp. 181–186, Dec 2013.
[5] H. Nakada, H. Ogawa, and T. Kudoh, “Stream processing with bigdata: Sss-mapreduce,” in Cloud Computing Technology and Science (CloudCom), 2012 IEEE 4th International Conference on, pp. 618–621, Dec 2012.
[6] T. Chardonnens, “Big data analytics on high velocity streams,” Master’s thesis, University of Fribourg (Switzerland), June 2013.
[7] F. Amato, A. De Santo, F. Gargiulo, V. Moscato, F. Persia, A. Picariello, and S. Poccia, “Semtree: An index for supporting semantic retrieval of documents,” in Data Engineering Workshops (ICDEW), 2015 31st IEEE International Conference on, pp. 62–67, April 2015.
[8] Cambazoglu BB, Kayaaslan E, Jonassen S, Aykanat C (2013) “A term-based inverted index partitioning model for efficient distributed query processing”. ACM Trans Web 7(3):1–23. doi:10.1145/2516633.
[9] Bast H, CelikikM(2013) “Efficient fuzzy search in large text collections”. ACM Trans Inf Syst 31(2):1–59. doi:10.1145/2457465.2457470