Total views : 88
Structured Parallel Efficient Execution Database Management System Over Enormous Dataset with MapReduce using Matlab
Objective: MapReduce is an encoding representation and a connected execution for handing out and generate huge data set. The objective of the present paper is that retrieve the data from enormous dataset in efficient manner a MapReduce. Methodology: The present paper uses structured parallel efficient execution Database Management System i.e. Parallel Database Management Systems (PDBMS). The present paper uses the Matlab for implementing PDBMS. This paper uses the broad concept of the paradigms quite than the exact implementations of MapReduce and Parallel DBMS. Such enormous information investigation on large clusters present new opportunity and challenge for mounting an extremely scalable and competent dispersed calculation system which is informal to strategy and multi- composite scheme optimization to exploit presentation and dependability to conquer this problem realize a new algorithm called Structured Parallel Efficient Execution Database 'Management (SPEED'MS) System' over Enormous Dataset with MapReduce. Findings: An optimizer is answerable for converting script into well-organized implementation plans for the dispersed calculation engine. Speed is living thing utilized day by day for assorted qualities of data study and data mining applications driving Bing, and other online services. The algorithm has been tested with the Matlab. Applications: MapReduce concept has potential applications like Clinical big data analysis, Bioinformatics Distributed programming.
DBMS, Enormous Dataset Speed, MapReduce, Parallel DBMS.
- Borkar V, Carey M, Grover M, Onose RN, Vernica R. Hyracks: a flexible and extensible foundation for dataintensive computing. The Proceedings of ICDE Conference. 2011; p. 165-78. Crossref
- Chattopadhyay B LinLLiu W, Mittal S, Aragonda, PLychagina, VKwon, YWong, Tenzing M. A SQL implementation on the MapReduce framework. The Proceedings of VLDB Conference. 2011; p. 235-45.
- Jain, Anil K, Murty MN, Patrick J. Flynn: Data clustering: a review. ACM computing surveys (CSUR). 1999; 31(3): 264323.
- Abouzeid A, Bajda-Pawlikowski, Abadi K, Silberschatz D. A Rasin: Hadoop DB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads. The Proceeding of VLDB Conference. 2009; p. 1-12.
- Narayanan A, Kandula G, Greenberg S, Stoica A, Lu I, Saha Y, Harris B. Reining in the outliers in Map-reduce clusters using Mantri. The Proceedings of OSDI Conference. 2010; p. 1-14. PMCid:PMC2825503
- Bandyopadhyay, Sanghamitra, Maulik U: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition. 2002; 35(6):1197208. Crossref
- Abadi DJ, Myers DS, DeWitt DJ, Madden S. Materialization Strategies in a Column-Oriented DBMS. The Proceedings of the 23rd IEEE International Conference on Data Engineering. 2007; p. 466-75. Crossref
- David SJ. Multiple objective optimization with vector evaluated genetic algorithms. Pittsburgh, USA: PA: The Proceedings of the 1st International Conference on Genetic Algorithms. 1985 July; p. 93-100.
- Abadi DJ, Ahmad Y, Balazinska M, Çetintemel U, Cherniack M, Hwang JH, Lindner W, Anurag Maskey, Rasin A, Ryvkina E, Nesime Tatbul, Xing Y, Stanley B Zdonik. The Design of the Borealis Stream Processing Engine. The Proceedings of the 3rd Biennial Conference on Innovative Data Systems Research. 2005; p. 127-30.
- Mackey, Grant, Sehrish S, Wang J. Improving metadata management for small files in HDFS. CLUSTER’09: 2009: Cluster Computing and Workshops. IEEE International Conference. 2009; p. 1-4. PMCid:PMC2891371
- Apache handoop. Date accessed: 22/03/2017: Available from: http://hadoop.apache.org/>.
- Copeland GP, Khoshafian SN. A decomposition storage model. The Proceedings of SIGMOD Conference. 1985; p. 1-12. Crossref Crossref
- Chaiken, Jenkins, Larson, Ramsey, Shakib, Weaver, Zhou. SPEED: easy and efficient parallel processing of massive data sets. The Proceedings of VLDB Conference. 2008; p. 1-12.
- Darwen. The role of functional dependencies in query decomposition. In: Relational Database Writings 19891991. Addison Wesley. 1992; p. 133-54.
- Fries, Sergie. MapReduce: Fast Access to Complex Data Management and Data Exploration Group. 2014 July; p. 1-15.
- Battre D, Ewen S, Kao HF, OMarkl, Warneke D. A programming model and execution framework for web-scale analytical processing. The Proceedings of the ACM Symposium on Cloud Computing. 2010; p. 119-30. Crossref
- Beyer KS, Ercegovac V, Gemulla R, Balmin A, Eltabakh M, KanneC, Ozcan. A scripting language for large scale semi structured data analysis. The Proceedings of VLDB Conference. 2011; p. 1-12. PMCid:PMC3035580
- Dean, Jeffery, Sanjay. Ghemawat: MapReduce: Simplified Data Processing on Large Clusters. Google Research Publication. 2014 May; p.1-13.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.