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MAP RANKING AND REDUCING WITH HADOOP IN BIG DATA ANALYSIS
  • Safiye Turgay,
  • Suat Erdoğan
Safiye Turgay
Sakarya University
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Suat Erdoğan
Sakarya University

Corresponding Author:[email protected]

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Abstract

Today, with the rapidly developing technology, data volume and data sharing are increasing day by day. Data mapping and reducing is very important in the process of analyzing big data which in faster and more effectively. In big data analysis, data mapping sequence and mining works by using a specific algorithm the inputs and lists of values as parameters. All values in the system entered for the intermediate results which are converted and created. In the mapping process, the data is subjected to fast sorting processes, taking into account the area that occupied with the number of repetitions. Processing small amounts of data has a cost-reducing effect on issues such as less time, memory, process and disk consumption. In this study, data sorting and data reduction operations were performed more effectively with the proposed algorithm. The data prepared for data analysis with their sortable features and then the process applied. Considering the data size and value structures have been ensured that the reduction process applied to each data reason of large amounts data processed at a much higher cost. This process facilitated the selection of samples are also easier. Software has been developed to realize with new subprogram which was arranged by using of the Hadoop library. The value file transferred and reduced to certain limits and then sequencing and mapping. Indexing has been made in the mapping output. The mapping and reducing classes working in parallel and an output file formed as a result of the evaluation.