Service aware resource management into cloudlets for data offloading towards IoT

Roy, Deepsubhra Guha; Mahato, Bipasha; Ghosh, Ahona; De, Debashis

doi:10.1007/s00542-019-04450-y

Cited by 6 publications

(1 citation statement)

References 23 publications

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“…The field of science and technology often involves many disciplines and belongs to different types; actual information management is more difficult, especially in the current cloud environment background; the actual scientific and technological information management requirements in the continuous enhancement at this time should be a higher scientific and technological management of the data service model to operate, so as to enhance the effectiveness of the actual scientific and technological innovation [9][10].…”

Section: Introductionmentioning

confidence: 99%

Research on the Innovation of Science and Technology Management Data Service Mode under Artificial Intelligence Technology

Xiao,

2024

Applied Mathematics and Nonlinear Sciences

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S&T management data has a wide range of sources and types, and the innovation of S&T management data service model is an important way for the efficient utilization of S&T resources in the new era, so this paper creates an innovative model of S&T management data service based on Data-Information-Knowledge-Wisdom model and artificial intelligence technology. Heterogeneous data mining technology based on association rules is used to obtain the connection between S&T management data, Lagrange interpolation is used for heterogeneous data cleaning to predict the missing values of S&T management data, and data lineage resolution technology is used to solve the challenges brought by the complex and diverse S&T management data components. Experimental analyses are conducted from both S&T management data processing and data service cases to verify the effectiveness and scientificity of the S&T management data service innovation model proposed in this paper. The results show that in S&T management data processing, this paper’s method consumes less than 2.45s for associated data rule mining, which has high mining efficiency, and the duplication rate and missing rate of data are below 0.0260 and 0.0222. Through the analysis of the service quality data, it can be seen that the degree of explanation of the service process quality problems of the model proposed in this paper tends to be close to 1, which can reflect the differences in the service process quality problems, and provide accurate, intelligent and personalized services for the main body of science and technology innovation.

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Section: Introductionmentioning

confidence: 99%