Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data
We live in an age where everything around us is being created. Data generation rates are so scary, creating pressure to implement costly and straightforward data storage and recovery processes. MapReduce model functionality is used for creating a cluster parallel, distributed algorithm, and large da...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020026 |
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| author | Ankit Kumar Neeraj Varshney Surbhi Bhatiya Kamred Udham Singh |
| author_facet | Ankit Kumar Neeraj Varshney Surbhi Bhatiya Kamred Udham Singh |
| author_sort | Ankit Kumar |
| collection | DOAJ |
| description | We live in an age where everything around us is being created. Data generation rates are so scary, creating pressure to implement costly and straightforward data storage and recovery processes. MapReduce model functionality is used for creating a cluster parallel, distributed algorithm, and large datasets. The MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster results. Expected results are obtained in the work and the exam conducted under this job; many of them are scheduled to set schedules, match matrices’ data positions, clustering before determining to click, and accurate mapping and internal reliability to be closed together to avoid running and execution times. Mapper output and proponents have been implemented, and the map has been used to reduce the function. The execution input key/value pair and output key/value pair have been set. This paper focuses on evaluating this technique for the efficient retrieval of large volumes of data. The technique allows for capabilities to inform a massive database of information, from storage and indexing techniques to the distribution of queries, scalability, and performance in heterogeneous environments. The results show that the proposed work reduces the data processing time by 30%. |
| format | Article |
| id | doaj-art-4e1f5f356cc2423c8b7c4320edfdce1e |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-4e1f5f356cc2423c8b7c4320edfdce1e2025-02-03T02:57:52ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-12-016446547710.26599/BDMA.2022.9020026Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big DataAnkit Kumar0Neeraj Varshney1Surbhi Bhatiya2Kamred Udham Singh3Department of Computer Engineering and Application, GLA University, Mathura 281406, IndiaDepartment of Computer Engineering and Application, GLA University, Mathura 281406, IndiaCollege of Computer Sciences and Information Technology, King Faisal University, Hofuf 31982, Saudi ArabiaSchool of Computing, Graphic Era Hill University, Dehradun 248002, IndiaWe live in an age where everything around us is being created. Data generation rates are so scary, creating pressure to implement costly and straightforward data storage and recovery processes. MapReduce model functionality is used for creating a cluster parallel, distributed algorithm, and large datasets. The MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster results. Expected results are obtained in the work and the exam conducted under this job; many of them are scheduled to set schedules, match matrices’ data positions, clustering before determining to click, and accurate mapping and internal reliability to be closed together to avoid running and execution times. Mapper output and proponents have been implemented, and the map has been used to reduce the function. The execution input key/value pair and output key/value pair have been set. This paper focuses on evaluating this technique for the efficient retrieval of large volumes of data. The technique allows for capabilities to inform a massive database of information, from storage and indexing techniques to the distribution of queries, scalability, and performance in heterogeneous environments. The results show that the proposed work reduces the data processing time by 30%.https://www.sciopen.com/article/10.26599/BDMA.2022.9020026big datahadoopmapreduceresource allocationquery management |
| spellingShingle | Ankit Kumar Neeraj Varshney Surbhi Bhatiya Kamred Udham Singh Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data Big Data Mining and Analytics big data hadoop mapreduce resource allocation query management |
| title | Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data |
| title_full | Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data |
| title_fullStr | Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data |
| title_full_unstemmed | Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data |
| title_short | Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data |
| title_sort | replication based query management for resource allocation using hadoop and mapreduce over big data |
| topic | big data hadoop mapreduce resource allocation query management |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020026 |
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