Dear Readers,
I am sharing my recently published article "Implementation and Analysis of MapReduce on Biomedical Big Data" via this post. Post has been written in the following
1. Affiliation
2. Abstract
3. Keywords
4. Reference
5. DOI
Source: Indian Journal of Science and Technology
Affiliations
Cognizant Technology Solutions, Chennai - 600028, Tamil Nadu, India
System Design Associate, Global Knowledge Network India Private Limited, Chennai, India
Department of Computer Science, Sri Venkateswara College of Engineering, Tirupati – 517502, Andhra Pradesh, India
Sathyabama University, Chennai – 600119, Tamil Nadu, India
Amazon Development Center, Chennai, Tamil Nadu, India
Abstract
Organizing and maintaining the big data are the two major concerns which have led to many challenges for the organization. The main objective of this research work is to give an overall idea about organizing Big data with High performance. MapReduce is one of the commonly used techniques which is used to analyze a large volume of data in an efficient manner. A common overview of Big data and the implementation of the MapReducing technique on Biomedical Big Data has been discussed in this paper with an algorithm. Discussion on performance analysis of MapReducing technique being will open the doors for further research activities in Big Data Analytics and MapReducing technique. Highlight of this research work is the data which has been selected and the output of the research work has been openly discussed to help the beginners of Big data. The proposed research work will give an insight about the implementation of Hadoop Distributed File System for small and medium sized business.
Keywords
Big Data, Big Data analytics, Biomedical Data Analysis, MapReduce, Performance
References
IBM. Big Data at the Speed of Business. Available from: http://www-01.ibm.com/software/data/bigdata/
Gantz J, Reinsel D. The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC analyze the future. 2012 Dec; 2007:1–6.
Liu S. Exploring the future of computing. IT Professional. 2013:2–3.
Big data at CSAIL. Available from: http://bigdata.csail.mit.edu/
Oracle. Oracle big data for the enterprise; 2012. Available from: http://www.oracle.com/caen/technologies/big-data
Centre for Development of Advanced Computing. Available from: http://cdac.in/
Wu X, Zhu X, Wu GQ, Ding W. Data mining with big data. IEEE Transactions on Knowledge and Data Engineering. 2014 Jan; 26(1):97–107.8. Manyika J, et al. Big data: The next frontier for innovation, competition, and productivity. San Francisco, CA, USA: McKinsey Global Institute; 2011. p. 1–137.
Hu H, et al. Towards scalable systems for big data analytics: A technology tutorial; 2014.
Zikopoulos P, Eaton C. Understanding big data: Analytics for enterprise class hadoop and streaming data. New York, NY, USA: McGraw-Hill; 2011.
Jiang D, Tung AK, Chen G. Map-join-reduce: Toward scalable and efficient data analysis on large clusters. IEEE Transactions on Knowledge and Data Engineering. 2011 Sep; 23(9):1299–311.
Rajendran PK, Muthukumar B, Nagarajan G. Hybrid intrusion detection system for private cloud: A systematic approach. Procedia Computer Science. 2015; 48:325–9.
Muthukumar B, Rajendran PK. Intelligent intrusion detection system for private cloud environment. Security in Computing and Communications. Springer International Publishing; 2015 Aug 10. p. 54–65.
Asbern A, Asha P. Performance evaluation of association mining in Hadoop single node cluster with Big Data. 2015 International Conference on Circuit, Power and Computing Technologies (ICCPCT); 2015 Mar 19. p. 1–5.
Kailasam S, Dhawalia P, Balaji SJ, Iyer G, Dharanipragada J. Extending MapReduce across Clouds with BStream. IEEE Transactions on Cloud Computing. 2014 Jul 1; 2(3):362–76.
Lakshmi M, Sowmya K. Sensitivity analysis for safe grainstorage using big data. Indian Journal of Science and Technology. 2015 Apr 1; 8(S7):156–64.
Kim BS, Kim TG, Song HS. Parallel and distributed framework for standalone monte carlo simulation using mapreduce. Indian Journal of Science and Technology. 2015 Oct; 8(25).
Noh K-H Lee D-S. Bigdata platform design and implementation model. Indian Journal of Science and Technology. 2015 Aug; 8(18).
Dhamodaran S, Sachin KR, Kumar R. Big data implementation of natural disaster monitoring and alerting system in real time social network using hadoop technology. Indian Journal of Science and Technology. 2015 Sep; 8(22).
Rajendran PK, Rajesh M, Abhilash R. Hybrid Intrusion Detection Algorithm for Private Cloud. Indian Journal of Science and Technology. 2015 Dec; 8:35.
Rajesh M, Abhilash R, Kumar RP. URL ATTACKS: Classification of URLs via Analysis and Learning. International Journal of Electrical and Computer Engineering (IJECE). 2016 Jun 1; 6(3).
Muthukumar B ,Praveen Kumar Rajendran, S. Murugan, G.Nagarajan. Multilevel Intrusion Detection System for Private Cloud Environment. Advances in Intelligent Systems and Computing(Accepted).
DOI: http://dx.doi.org/10.17485/ijst%2F2016%2Fv9i31%2F83451
Thanks and Regards
Praveen Kumar Rajendran,
Chennai
I am sharing my recently published article "Implementation and Analysis of MapReduce on Biomedical Big Data" via this post. Post has been written in the following
1. Affiliation
2. Abstract
3. Keywords
4. Reference
5. DOI
Source: Indian Journal of Science and Technology
Affiliations
Cognizant Technology Solutions, Chennai - 600028, Tamil Nadu, India
System Design Associate, Global Knowledge Network India Private Limited, Chennai, India
Department of Computer Science, Sri Venkateswara College of Engineering, Tirupati – 517502, Andhra Pradesh, India
Sathyabama University, Chennai – 600119, Tamil Nadu, India
Amazon Development Center, Chennai, Tamil Nadu, India
Abstract
Organizing and maintaining the big data are the two major concerns which have led to many challenges for the organization. The main objective of this research work is to give an overall idea about organizing Big data with High performance. MapReduce is one of the commonly used techniques which is used to analyze a large volume of data in an efficient manner. A common overview of Big data and the implementation of the MapReducing technique on Biomedical Big Data has been discussed in this paper with an algorithm. Discussion on performance analysis of MapReducing technique being will open the doors for further research activities in Big Data Analytics and MapReducing technique. Highlight of this research work is the data which has been selected and the output of the research work has been openly discussed to help the beginners of Big data. The proposed research work will give an insight about the implementation of Hadoop Distributed File System for small and medium sized business.
Keywords
Big Data, Big Data analytics, Biomedical Data Analysis, MapReduce, Performance
References
IBM. Big Data at the Speed of Business. Available from: http://www-01.ibm.com/software/data/bigdata/
Gantz J, Reinsel D. The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC analyze the future. 2012 Dec; 2007:1–6.
Liu S. Exploring the future of computing. IT Professional. 2013:2–3.
Big data at CSAIL. Available from: http://bigdata.csail.mit.edu/
Oracle. Oracle big data for the enterprise; 2012. Available from: http://www.oracle.com/caen/technologies/big-data
Centre for Development of Advanced Computing. Available from: http://cdac.in/
Wu X, Zhu X, Wu GQ, Ding W. Data mining with big data. IEEE Transactions on Knowledge and Data Engineering. 2014 Jan; 26(1):97–107.8. Manyika J, et al. Big data: The next frontier for innovation, competition, and productivity. San Francisco, CA, USA: McKinsey Global Institute; 2011. p. 1–137.
Hu H, et al. Towards scalable systems for big data analytics: A technology tutorial; 2014.
Zikopoulos P, Eaton C. Understanding big data: Analytics for enterprise class hadoop and streaming data. New York, NY, USA: McGraw-Hill; 2011.
Jiang D, Tung AK, Chen G. Map-join-reduce: Toward scalable and efficient data analysis on large clusters. IEEE Transactions on Knowledge and Data Engineering. 2011 Sep; 23(9):1299–311.
Rajendran PK, Muthukumar B, Nagarajan G. Hybrid intrusion detection system for private cloud: A systematic approach. Procedia Computer Science. 2015; 48:325–9.
Muthukumar B, Rajendran PK. Intelligent intrusion detection system for private cloud environment. Security in Computing and Communications. Springer International Publishing; 2015 Aug 10. p. 54–65.
Asbern A, Asha P. Performance evaluation of association mining in Hadoop single node cluster with Big Data. 2015 International Conference on Circuit, Power and Computing Technologies (ICCPCT); 2015 Mar 19. p. 1–5.
Kailasam S, Dhawalia P, Balaji SJ, Iyer G, Dharanipragada J. Extending MapReduce across Clouds with BStream. IEEE Transactions on Cloud Computing. 2014 Jul 1; 2(3):362–76.
Lakshmi M, Sowmya K. Sensitivity analysis for safe grainstorage using big data. Indian Journal of Science and Technology. 2015 Apr 1; 8(S7):156–64.
Kim BS, Kim TG, Song HS. Parallel and distributed framework for standalone monte carlo simulation using mapreduce. Indian Journal of Science and Technology. 2015 Oct; 8(25).
Noh K-H Lee D-S. Bigdata platform design and implementation model. Indian Journal of Science and Technology. 2015 Aug; 8(18).
Dhamodaran S, Sachin KR, Kumar R. Big data implementation of natural disaster monitoring and alerting system in real time social network using hadoop technology. Indian Journal of Science and Technology. 2015 Sep; 8(22).
Rajendran PK, Rajesh M, Abhilash R. Hybrid Intrusion Detection Algorithm for Private Cloud. Indian Journal of Science and Technology. 2015 Dec; 8:35.
Rajesh M, Abhilash R, Kumar RP. URL ATTACKS: Classification of URLs via Analysis and Learning. International Journal of Electrical and Computer Engineering (IJECE). 2016 Jun 1; 6(3).
Muthukumar B ,Praveen Kumar Rajendran, S. Murugan, G.Nagarajan. Multilevel Intrusion Detection System for Private Cloud Environment. Advances in Intelligent Systems and Computing(Accepted).
DOI: http://dx.doi.org/10.17485/ijst%2F2016%2Fv9i31%2F83451
Thanks and Regards
Praveen Kumar Rajendran,
Chennai
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