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Analysis of Techniques to Retrieve Big Database

S. Puri1 , L. Jain2 , O.P. Gupta3

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-9 , Page no. 66-71, Sep-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i9.6671

Online published on Sep 30, 2019

Copyright © S. Puri, L. Jain, O.P. Gupta . 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: S. Puri, L. Jain, O.P. Gupta, “Analysis of Techniques to Retrieve Big Database,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.9, pp.66-71, 2019.

MLA Style Citation: S. Puri, L. Jain, O.P. Gupta "Analysis of Techniques to Retrieve Big Database." International Journal of Computer Sciences and Engineering 7.9 (2019): 66-71.

APA Style Citation: S. Puri, L. Jain, O.P. Gupta, (2019). Analysis of Techniques to Retrieve Big Database. International Journal of Computer Sciences and Engineering, 7(9), 66-71.

BibTex Style Citation:
@article{Puri_2019,
author = {S. Puri, L. Jain, O.P. Gupta},
title = {Analysis of Techniques to Retrieve Big Database},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2019},
volume = {7},
Issue = {9},
month = {9},
year = {2019},
issn = {2347-2693},
pages = {66-71},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4851},
doi = {https://doi.org/10.26438/ijcse/v7i9.6671}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i9.6671}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4851
TI - Analysis of Techniques to Retrieve Big Database
T2 - International Journal of Computer Sciences and Engineering
AU - S. Puri, L. Jain, O.P. Gupta
PY - 2019
DA - 2019/09/30
PB - IJCSE, Indore, INDIA
SP - 66-71
IS - 9
VL - 7
SN - 2347-2693
ER -

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Abstract

In today’s world there are a large amount of data which need to be processed with big databases. In recent years, increase plethora of companies has adopted different-different types of non-relational database. The goal of this research is to implement techniques to retrieve big database for the big datasets and investigate the performance of the big database techniques on CPU utilization and high-performance computing software. It attempts to use NoSQL database to replace the relational database. In this research mainly focuses on the new technology of NoSQL database i.e. MongoDB, HadoopDB. Performance comparison of two big data techniques is carried out. The result found that Aggregation technique consumes less execution time than MapReduce technique and more efficient with MongoDB database where as MapReduce technique has less efficient with HadoopDB. Aggregation technique also produces fine relevant information results with less CPU utilization. The result also shows that MongoDB has the capability to switch SQL databases as compare to HadoopDB.

Key-Words / Index Term

Big Data, MongoDB, HadoopDB, Aggregation, MapReduce

References

[1] Kirti, M Pardeep, “Database for unstructured, semistructured data- NoSQL”, International journal of advanced research in computer engineering & technology, Vol. 4, Issue.2, pp. 466-469, 2015.
[2] A Ait-Mlouk, F Gharnati, T Agouti, “Application of big data analysis with decision tree for road accident”, Indian Journal of Science Technology, Vol. 10, Issue.29, pp. 1-10, 2017.
[3] N Rajyaguru, M Vinay, “A comparative study of big data on mobile computing”, Indian Journal of Science and Technology, Vol. 10, Issue.21, pp. 1-7, 2017.
[4] A Kamilaris, A Kartakoullis, B X. F Prenafeta, “A review on the big data analysis in agriculture”, Computer and Electronics in Agriculture, Vol. 143, pp. 23-27, 2017.
[5] Dean J and Ghemawat S (2008) MapReduce: Simplified Data Processing on Large Clusters. 137-150.
[6] Dede E, Govindaraju M, Gunter D, Canon R S, Ramakrishan L (2013) Performance evaluation of a MongoDB and hadoop platform for scientific data analysis. 4th Workshop on Scientific Cloud Computing, ACM, pp. 13-20.
[7] Nunan D, Domenico M D (2013) Market research and the ethics of big data. International journal of market research, 55(4):505-520.
[8] Ozarkar K, Rajani R (2014) Optimization technique for efficient dynamic query forms with NoSQL. International journal of science and research, 3(11):2041-2044.
[9] Bhosale H S, Gadekar D P (2014) A review paper on big data and hadoop. International Journal of Scientific and Research Publications, 4(10):1-7.
[10] A D Arasteh, D Mohammadpur, M Meghdadi, “MapReduce based implementation of aggregate functions on Cassandra”, International journal of electronics communication and computer technology, Vol. 4, Issue.3, pp. 604-609, 2014.
[11] R Zuech, M T Khoshgoftaar and R Wald, “Intrusion detection and big heterogeneous data a survey”, Journal of Big Data, Vol.2, Issue.3, pp. 2-41, 2015.
[12] Z Mo, Y Li, “Research of big data based on the views of technology and application”, American journal of industrial and business management, Vol.5, pp. 192-197, 2015.
[13] V S Thiyagarajan, A Ayyasamy, “Privacy preserving over big data through Vssfa and Map-Reduce framework in cloud environment”, Indian Journal of Wireless Personal Communication, Vol. 97, Issue.4, pp. 6239-63, 2017.
[14] K Abouelmehdi, H A Beni and H Khaloufi, “Big healthcare data: preserving security and privacy”, Journal of Big Data, Vol. 5, pp. 1-18, 2018.
[15] M S A Khan, H Jamshed, S Bano, N M Anwar, “Big data management in connected world of Internet of things”, Indian Journal of Science Technology, Vol. 10, Issue.29, pp. 1-9, 2017.
[16] V. M A Martin, K David, A Vignesh, “Big Data and its challenges”, International journal of scientific research in computer science, engineering and information technology, Vol. 3, Issue.3, pp. 533-538, 2018.
[17] M Chevalier, M E Malki, A Kopliku, O Teste, R Tournier, “Implementing Multidimensional Data Warehouses into NoSQL”, ICEIS, Vol. 1, pp. 172-183, 2015.
[18] L Kumar, S Rajawat, K Joshi, “Comparative analysis of NoSQL (MongoDB) with MySQL Database”, International Journal of Modern Trends in Engineering and Research, Vol.2, Issue. 5, pp. 120-127, 2015.