Cloud Based Big Data Processing Approaches
Rashmi. G1 , S. Sathish Kumar2
Section:Research Paper, Product Type: Journal Paper
Volume-04 ,
Issue-04 , Page no. 5-8, Jun-2016
Online published on Jun 29, 2016
Copyright © Rashmi. G, S. Sathish Kumar . 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 Citation
IEEE Style Citation: Rashmi. G, S. Sathish Kumar , “Cloud Based Big Data Processing Approaches,” International Journal of Computer Sciences and Engineering, Vol.04, Issue.04, pp.5-8, 2016.
MLA Citation
MLA Style Citation: Rashmi. G, S. Sathish Kumar "Cloud Based Big Data Processing Approaches." International Journal of Computer Sciences and Engineering 04.04 (2016): 5-8.
APA Citation
APA Style Citation: Rashmi. G, S. Sathish Kumar , (2016). Cloud Based Big Data Processing Approaches. International Journal of Computer Sciences and Engineering, 04(04), 5-8.
BibTex Citation
BibTex Style Citation:
@article{G_2016,
author = {Rashmi. G, S. Sathish Kumar },
title = {Cloud Based Big Data Processing Approaches},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2016},
volume = {04},
Issue = {04},
month = {6},
year = {2016},
issn = {2347-2693},
pages = {5-8},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=94},
publisher = {IJCSE, Indore, INDIA},
}
RIS Citation
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=94
TI - Cloud Based Big Data Processing Approaches
T2 - International Journal of Computer Sciences and Engineering
AU - Rashmi. G, S. Sathish Kumar
PY - 2016
DA - 2016/06/29
PB - IJCSE, Indore, INDIA
SP - 5-8
IS - 04
VL - 04
SN - 2347-2693
ER -




Abstract
Nowadays Big data processing in cloud has become a challenging task. This paper describes the basics of cloud computing and current big-data processing approaches in cloud such as batch-based, stream-based, graph-based, DAG-based, interactive-based, visual-based and summarizes the strengths and weaknesses of these approaches in order to help the future big data research scholars to select the appropriate processing technique.
Key-Words / Index Term
Big data, Cloud, Data processing in cloud, Big data processing, Big Data processing approaches
References
[1] B. D. Martino, R. Aversa, G. Cretella, et al., “Big data (lost) in the cloud,” International Journal of Big Data Intelligence, vol. 1, no. 1, pp. 3–17, 2014. doi:10.1504/IJBDI.2014.063840.
[2] A. A. Chandio, F. Zhang, and T.D. Memon, “Study on LBS for characterization and analysis of big data benchmarks,” Mehran University Research Journal of Engineering and Technology, vol. 33, no. 4, pp. 432–440, Oct. 2014.
[3] GigaSpaces. (2013). Big Data Survey [Online]. Available: http://www.gigaspaces.com
[4] Q. Li, T. Zhang, and Y. Yu, “Using cloud computing to process intensive floating car data for urban traffic surveillance,” International Journal of Geographical Information Science, vol. 25, no. 8, pp. 1303–1322, Aug. 2011. doi: 10.1080/13658816.2011.577746.
[5] Z. Li, C. Chen, and K. Wang, “Cloud computing for agent-based urban transportation systems,” IEEE Intelligent Systems, vol. 26, no. 1, pp. 73–79, 2011. doi: 10.1109/MIS.2011.10.
[6] S. Seo, E. J. Yoon, J. Kim, et al., “Hama: an efficient matrix computation with the mapreduce framework,” in IEEE Second International Conference on Cloud Computing Technology and Science, Indianapolis, USA, 2010, pp. 721–726. doi:10.1109/CloudCom.2010.17.
[7] G. Malewicz, M. H. Austern, A. J. C Bik, et al., “Pregel: a system for large-scale graph processing,” in ACM SIGMOD International Conference on Management of Data, Indianapolis, USA, 2010, pp. 135–146. doi: 10.1145/1807167.1807184.
[8] M. Isard, M. Budiu, Y. Yu, et al., “Dryad: distributed data-parallel programs from sequential building blocks,” in EuroSys'07, Lisboa, Portugal, 2007.
[9] Apache. (2013). Apache Mahout [Online]. Available: http://mahout.apache.org/
[10] Pentaho. (2013). Pentaho Big Data Analytics [Online]. Available: http://www.pentaho.com/product/big-data-analytics.
[11] Skytree. (2013). Skytree The Machine Learning Company [Online]. Available:http://www.skytree.net/
[12] Karmasphere. (2012). FICO Big Data Analyzer [Online] Available:http://www.karmasphere.com/
[13]Datameer. (2013). Datameer [Online]. Available: http://www.datameer.com/
[14]Cloudera. (2013). Cloudera [Online]. Available: http://www.cloudera.com/
[15] Apache. (2012). Apache Storm Project [Online]. Available: http://www.stormproject.net
[16]L. Neumeyer, B. Robbins, A. Nair, et al., “S4: distributed stream computing platform,” in IEEE International Conference on Data Mining Workshops, Sydney, Australia, 2010, pp. 170–177. doi: 10.1109/ICDMW.2010.172.
[17] SQLstrean. (2012). SQLstream s-Server [Online]. Available:http://www.sqlstream.com/blaze/s-server/
[18] Apache. (2011). Apache Giraph [Online]. Available: http://giraph.apache.org/
[19] Tableau. (2013). Tableau [Online]. Available: http://www.tableausoftware.com/
[20] Talend. (2009). Talend Open Studio [Online]. Available: https://www.talend.com/
[21] Aftab A. Chandio, Nikos Tziritas, Cheng-Zhong Xu, “Big-Data Processing Techniques and Their Challenges in Transport Domain”, DOI: 10.3969/j.issn.1673-5188.2015.01.007
[22]Poornima Sharma,Varun Garg, Prof. Randeep Kaur, Prof. Satendra Sonare, “Big Data in Cloud Environment”, International Journal of Computer Sciences and Engineering, Volume-01, Issue-03, Page No (15-17), Nov -2013, E-ISSN:2347-2693.