activity
20222024
most citedOWL: A Large Language Model for IT Operations

4 citations · 8 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SE2024

ECLIPSE: Semantic Entropy-LCS for Cross-Lingual Industrial Log Parsing

Wei Zhang, Xianfu Cheng, Yi Zhang +8

Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks…

cs.MA2024★ 1 cited

mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecture

Wei Zhang, Hongcheng Guo, Jian Yang +8

Root cause analysis (RCA) in Micro-services architecture (MSA) with escalating complexity encounters complex challenges in maintaining system stability and efficiency due to fault…

cs.SE2024★ 2 cited

Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging

Wei Zhang, Xiangyuan Guan, Lu Yunhong +5

Logs produced by extensive software systems are integral to monitoring system behaviors. Advanced log analysis facilitates the detection, alerting, and diagnosis of system faults.…

cs.SE2024★ 1 cited

MLAD: A Unified Model for Multi-system Log Anomaly Detection

Runqiang Zang, Hongcheng Guo, Jian Yang +6

In spite of the rapid advancements in unsupervised log anomaly detection techniques, the current mainstream models still necessitate specific training for individual system dataset…

cs.CL2023★ 4 cited

OWL: A Large Language Model for IT Operations

Hongcheng Guo, Jian Yang, Jiaheng Liu +13

With the rapid development of IT operations, it has become increasingly crucial to efficiently manage and analyze large volumes of data for practical applications. The techniques o…

cs.SE2022

LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction

Hongcheng Guo, Yuhui Guo, Renjie Chen +7

Fully supervised log anomaly detection methods suffer the heavy burden of annotating massive unlabeled log data. Recently, many semi-supervised methods have been proposed to reduce…