Suchergebnisse - "Duan, Chiming"

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  1. 1

    LLMeLog: An Approach for Anomaly Detection based on LLM-enriched Log Events von He, Minghua, Jia, Tong, Duan, Chiming, Cai, Huaqian, Li, Ying, Huang, Gang

    ISSN: 2332-6549
    Veröffentlicht: IEEE 28.10.2024
    “… Log-based anomaly detection is an essential task in maintaining software reliability. Existing log-based anomaly detection approaches often consist of three …”
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  2. 2

    Weakly-Supervised Log-Based Anomaly Detection with Inexact Labels via Multi-Instance Learning von He, Minghua, Jia, Tong, Duan, Chiming, Cai, Huaqian, Li, Ying, Huang, Gang

    ISSN: 1558-1225
    Veröffentlicht: IEEE 26.04.2025
    “… Log-based anomaly detection is essential for maintaining software availability. However, existing log-based anomaly detection approaches heavily rely on …”
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  3. 3

    CSLParser: A Collaborative Framework Using Small and Large Language Models for Log Parsing von Hong, Weijie, Wu, Yifan, Zhang, Lingzhe, Duan, Chiming, Xiao, Pei, He, Minghua, Yang, Xixuan, Li, Ying

    ISSN: 2332-6549
    Veröffentlicht: IEEE 21.10.2025
    “… Log parsing is a prerequisite for log analysis. Recently, large language models (LLMs) have demonstrated high accuracy in log parsing. However, their frequent …”
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  4. 4

    AcLog: An Approach to Detecting Anomalies from System Logs with Active Learning von Duan, Chiming, Jia, Tong, Li, Ying, Huang, Gang

    ISSN: 2836-3868
    Veröffentlicht: IEEE 01.07.2023
    “… Log-based anomaly detection is an essential aspect of maintaining software reliability, particularly in the context of microservice systems. However, existing …”
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  5. 5

    LogCAE: An Approach for Log-based Anomaly Detection with Active Learning and Contrastive Learning von Xiao, Pei, Jia, Tong, Duan, Chiming, Cai, Huaqian, Li, Ying, Huang, Gang

    ISSN: 2332-6549
    Veröffentlicht: IEEE 28.10.2024
    “… Log-based anomaly detection plays a crucial role in maintaining the reliability of software systems. Unsupervised models are more suitable for real-world usage …”
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  6. 6

    Famos: Fault Diagnosis for Microservice Systems Through Effective Multi-Modal Data Fusion von Duan, Chiming, Yang, Yong, Jia, Tong, Liu, Guiyang, Liu, Jinbu, Zhang, Huxing, Zhou, Qi, Li, Ying, Huang, Gang

    ISSN: 1558-1225
    Veröffentlicht: IEEE 26.04.2025
    “… Accurately diagnosing the fault that causes the failure is crucial for maintaining the reliability of a microservice system after a failure occurs. Mainstream …”
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  7. 7

    AFALog: A General Augmentation Framework for Log-based Anomaly Detection with Active Learning von Duan, Chiming, Jia, Tong, Cai, Huaqian, Li, Ying, Huang, Gang

    ISSN: 2332-6549
    Veröffentlicht: IEEE 09.10.2023
    “… Log-based anomaly detection is becoming more and more important for maintaining the availability of modern microservice systems. Existing …”
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  8. 8

    EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection von Duan, Chiming, Jia, Tong, Yang, Yong, Liu, Guiyang, Liu, Jinbu, Zhang, Huxing, Zhou, Qi, Li, Ying, Huang, Gang

    ISSN: 2379-190X
    Veröffentlicht: IEEE 06.04.2025
    “… Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest …”
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