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Article
A Dependency-Aware Storage Schema Selection Mechanism for In-Memory Big Data Computing Frameworks
Artificial intelligence applications that greatly depend on deep learning and compute vision processing becomes popular. Their strong demands for low-latency or real-time services make Spark, an in-memory big ...
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Chapter and Conference Paper
Shadow Data: A Method to Optimize Incremental Synchronization in Data Center
With the continuous increase of data, the data center that plays the role of backup is facing the problem of energy hunger. In practice, to reduce the bandwidth, the local data is synchronized to the data cent...
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Article
A Task-Aware Fine-Grained Storage Selection Mechanism for In-Memory Big Data Computing Frameworks
In-memory big data computing, widely used in hot areas such as deep learning and artificial intelligence, can meet the demands of ultra-low latency service and real-time data analysis. However, existing in-mem...
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Article
Open AccessEnhancement of damaged-image prediction based on digital twin technology
Digital twins have revolutionized the field of image enhancement by applying their unique capabilities. A digital twin refers to a virtual replica of a physical object or system, which can be utilized to simul...