Automatic Voting Machine using Hadoop

Ms. Shireen Fatima, Goel Institute of Technology & Management, Lucknow, INDIA; Mr. Shivam Shukla ,Goel Institute of Technology & Management, Lucknow, INDIA

Hadoop, sqoop, Hive- Hql, Mapreduce, oozie

Voting style has been changed from the word counting papers to the electronically voting records. The system provides many advantages over the traditional voting system by reducing the voting process time and provides the performance in terms of more flexibility and accuracy. But there are some drawbacks also. The large volume of data takes a lot of time to process which affects the system performance. This huge amount of data can be stored, processed and analyzed in many ways but they require fast retrieval technique. Hadoop is considered as the best solution for handling big data which uses parallel computing techniques. Hadoop gives a complete administration apparatus to manage the huge data. That leads us to do this project using Hadoop and HIVE. The demand for Automatic Voting Machine is ever increasing and the system creates a huge amount of data. The results that are produced should be processed in an efficient way. Traditional data storage system and their data processing techniques are not really effective in handling Big Data. These data can take different forms like structured, unstructured and semi structured. The processing power of the machine is influence by the large data size. The system is fully automated and be able to handle extremely large volumes of data. The datasets are created using the application and are used to analyze through HIVE and OOZIE. In Automatic Voting Machine user enter his/her voter Id. Through voterid it will check in HIVE tables whether voter ID is valid or invalid. If he/she is invalid, script will exit or else again check whether the voter is coming for the first time or not. If voter is coming for second time then scirpt will exit and if he is coming for first time then voter can select the candidate (party name) of his/her choice and cast the vote. The project contains the sqoop command. This is schedule in oozie to bring the data from mysql (RDBMS) to HIVE table. If the voting is completed, here it is assumed that at 5 PM voting will be completed and voter will not be allowed to cast his/her vote. So whenever wrapper script will execute it will check whether it is 5 PM if not then voter can cast his vote or else result will be displayed in terms of counting of votes with respect to candidates. Even the system also gives the result in terms of winning percentage with great accuracy.
    [1] R. K. Nadesh, K. Arivuselvan, and Srinivasan Pathanjali, A Quantitative Review on Introducing the Election Process with Cloud Based Electronic Voting and Measuring the Performance using Map Reduce, Indian Journal of Science and Technology, Vol 9(39), DOI: 10.17485/ijst/2016/v9i39/85585, October 2016 [2] Apache Hadoop – Wikipedia https://en.wikipedia.org/wiki/Apache_Hadoop, http://en.wikipedia.org/wiki/Big_data [3] http://www.cnet.com/news/facebook-processes-more-than-500-tb-ofdata-daily/ [4] http://en.wikipedia.org/wiki/Apache_Hadoops [5] Zhouwei, Pierre Guillaume and Chi-Hung Chi. Cloud TPS: Scalable Transactions for web applications in the cloud. IEEE transactions of scalable computing. 2012 Dec; 5(04). [6] Megiba Jasmine R and Nishiba GM. Public Cloud secure group sharing and accessing in cloud computing. Indian Journal of Science and Technology. 2015 July; 8(15). [7] Bhosale Poonam, Vethaka Priyanaka, Thorat Lata, Archana Lomte. Identity Access Management using Multitier Cloud Infrastructure for secure online voting system. IJMRD 2015 March; 2(4) [8] Shymala K, Sunitha Rani T. An analysis on efficient resource allocation mechanism in cloud computing. Indian Journal of Science and Technology. 2015 May; 8(9). [9] Rama Satish KV, Kavya NP. Big Data Processing with harnessing Hadoop-MapReduce for Optimizing Analytical Workloads. IEEE 2014, International Conference on Contemporary Computing and Informatics. [10] Kyoo-Sungnoh and Doo-Sik-Lee. Bigdata platform design and implementation. Indian Journal of Science and Technology. 2015 Aug; 8(9). [11] http://wiki.apache.org/hadoop/JobTracker [12] Mohammad Hammoud and Majd F. Sakr, “Locality-aware reduce task scheduling for MapReduce," 3rd Int. Conf. on Cloud Computing Technology and Science (CloudCom), IEEE, pp. 570-576, 2011.
Paper ID: GRDJEV02I070046
Published in: Volume : 2, Issue : 7
Publication Date: 2017-07-01
Page(s): 51 - 58