Block Storage Optimization and Parallel Data Processing and Analysis of Product Big Data Based on the Hadoop Platform

The traditional distributed database storage architecture has the problems of low efficiency and storage capacity in managing data resources of seafood products. We reviewed various storage and retrieval technologies for the big data resources. A block storage layout optimization method based on the...

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Vydané v:Mathematical problems in engineering Ročník 2021; s. 1 - 14
Hlavní autori: Wang, Yajun, Cheng, Shengming, Zhang, Xinchen, Leng, Junyu, Liu, Jun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Hindawi 31.12.2021
John Wiley & Sons, Inc
Predmet:
Algorithms
Big Data
Computer centers
Data analysis
Data correlation
Data integration
Data processing
Feature extraction
Hash based algorithms
Mathematical problems
Online transaction processing
Optimization
Parallel processing
Relational data bases
Retrieval
Sales forecasting
Seafood
Spatial data
Storage capacity
Systems stability
ISSN:1024-123X, 1563-5147
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Abstract The traditional distributed database storage architecture has the problems of low efficiency and storage capacity in managing data resources of seafood products. We reviewed various storage and retrieval technologies for the big data resources. A block storage layout optimization method based on the Hadoop platform and a parallel data processing and analysis method based on the MapReduce model are proposed. A multireplica consistent hashing algorithm based on data correlation and spatial and temporal properties is used in the parallel data processing and analysis method. The data distribution strategy and block size adjustment are studied based on the Hadoop platform. A multidata source parallel join query algorithm and a multi-channel data fusion feature extraction algorithm based on data-optimized storage are designed for the big data resources of seafood products according to the MapReduce parallel frame work. Practical verification shows that the storage optimization and data-retrieval methods provide supports for constructing a big data resource-management platform for seafood products and realize efficient organization and management of the big data resources of seafood products. The execution time of multidata source parallel retrieval is only 32% of the time of the standard Hadoop scheme, and the execution time of the multichannel data fusion feature extraction algorithm is only 35% of the time of the standard Hadoop scheme.
AbstractList The traditional distributed database storage architecture has the problems of low efficiency and storage capacity in managing data resources of seafood products. We reviewed various storage and retrieval technologies for the big data resources. A block storage layout optimization method based on the Hadoop platform and a parallel data processing and analysis method based on the MapReduce model are proposed. A multireplica consistent hashing algorithm based on data correlation and spatial and temporal properties is used in the parallel data processing and analysis method. The data distribution strategy and block size adjustment are studied based on the Hadoop platform. A multidata source parallel join query algorithm and a multi-channel data fusion feature extraction algorithm based on data-optimized storage are designed for the big data resources of seafood products according to the MapReduce parallel frame work. Practical verification shows that the storage optimization and data-retrieval methods provide supports for constructing a big data resource-management platform for seafood products and realize efficient organization and management of the big data resources of seafood products. The execution time of multidata source parallel retrieval is only 32% of the time of the standard Hadoop scheme, and the execution time of the multichannel data fusion feature extraction algorithm is only 35% of the time of the standard Hadoop scheme.
Author Zhang, Xinchen
Wang, Yajun
Liu, Jun
Cheng, Shengming
Leng, Junyu
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Copyright Copyright © 2021 Yajun Wang et al.
Copyright © 2021 Yajun Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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Snippet The traditional distributed database storage architecture has the problems of low efficiency and storage capacity in managing data resources of seafood...
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SubjectTerms Algorithms
Big Data
Computer centers
Data analysis
Data correlation
Data integration
Data processing
Feature extraction
Hash based algorithms
Mathematical problems
Online transaction processing
Optimization
Parallel processing
Relational data bases
Retrieval
Sales forecasting
Seafood
Spatial data
Storage capacity
Systems stability
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Title Block Storage Optimization and Parallel Data Processing and Analysis of Product Big Data Based on the Hadoop Platform
URI https://dx.doi.org/10.1155/2021/3839800
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Volume 2021
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