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.
Highlights
Owing to the rapid expansion of seafood enterprises, condition-monitoring technologies for mariculture and seafood production and circulation have increased
The big data resources include the geographic information of mariculture, weather, site temperature, humidity, monitoring videos and images, experimental documents, breeding and processing data, production, and circulation data
Hadoop distributed file system (HDFS) and parallel programming framework Hadoop MapReduce [19], which are efficient in big data storage and processing and suitable to distributed storage and management of all types of resource data in ordinary computers, provide high data throughput for applications, which are appropriate for running applications with large data sets. ey have been widely used for mass data storage and analysis in Yahoo and Facebook [20, 21]. erefore, the Hadoop cloud-computing technology is an important option for the storage and processing of seafood big data resources
Summary
Owing to the rapid expansion of seafood enterprises, condition-monitoring technologies for mariculture and seafood production and circulation have increased. E performance requirements of data-processing servers are relatively high It can meet the daily management work, it is difficult to meet the data scale and resolution ratio demand of modeling analysis and advanced applications. If the real-time database system is adopted, it can meet the requirement of data acquisition resolution ratio, with the increase of data volume, the load rate of the server will be relatively high, while the cost of a high-performance computer cannot be afforded by the enterprises. Based on big data analysis, through centralized data management methods, seafood enterprises can perform seafood production and circulation facilitates mariculture breeding, which can conduct a dynamic assessment of the breeding process, dynamic evaluation for seedling growth and breeding environment, quality assessment of marine food, and the dynamic resource adjustment based on deployment process and order.
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