Yang Yang¹, Zhenying Qu¹, Zefan Yan¹, Zhipeng Gao^1,*, Ti Wang²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 735-757, 2024, DOI:10.32604/cmc.2023.045807

Abstract Nowadays, ensuring the quality of network services has become increasingly vital. Experts are turning to knowledge graph technology, with a significant emphasis on entity extraction in the identification of device configurations. This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms. Initially, an improved active learning approach is employed to select the most valuable unlabeled samples, which are subsequently submitted for expert labeling. This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set. Then the labeled samples are utilized to train the model… More >

Jiangyuan Yao¹, Zheng Jiang², Kaiwen Zou², Shuhua Weng¹, Yaxin Li³, Deshun Li¹, Yahui Li^4,*, Xingcan Cao⁵

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 293-311, 2023, DOI:10.32604/cmc.2023.031282

Abstract With the expansion of network services, large-scale networks have progressively become common. The network status changes rapidly in response to customer needs and configuration changes, so network configuration changes are also very frequent. However, no matter what changes, the network must ensure the correct conditions, such as isolating tenants from each other or guaranteeing essential services. Once changes occur, it is necessary to verify the after-changed network. Whereas, for the verification of large-scale network configuration changes, many current verifiers show poor efficiency. In order to solve the problem of multiple global verifications caused by frequent updates of local configurations in… More >