The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index.
Drain: An Online Log Parsing Approach with Fixed Depth Tree
Abstract
Logs, which record valuable system runtime information, have been widely employed in Web service management by service providers and users. A typical log analysis based Web service management procedure is to first parse raw log messages because of their unstructured format; and then apply data mining models to extract critical system behavior information, which can assist Web service management. Most of the existing log parsing methods focus on offline, batch processing of logs. However, as the volume of logs increases rapidly, model training of offline log parsing methods, which employs all existing logs after log collection, becomes time consuming. To address this problem, we propose an online log parsing method, namely Drain, that can parse logs in a streaming and timely manner. To accelerate the parsing process, Drain uses a fixed depth parse tree, which encodes specially designed rules for parsing. We evaluate Drain on five real-world log data sets with more than 10 million raw log messages. The experimental results show that Drain has the highest accuracy on four data sets, and comparable accuracy on the remaining one. Besides, Drain obtains 51.85%~81.47% improvement in running time compared with the state-of-the-art online parser. We also conduct a case study on an anomaly detection task using Drain in the parsing step, which determines the effectiveness of Drain in log analysis.
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“This makes the agent's reasoning inspectable: a reviewer can see which logs, metrics, traces, tickets, configurations, validators, or simulators were queried, and whether the resulting evidence was sufficient for the proposed diagnosis or action [77], [60], [78], [61], [25].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [78]
In the citing paper
“First, modern production diagnosis is fundamentally query-driven, and good teams treat incident response as repeated hypothesis testing under partial observability [4], [78], [84].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [78]
In the citing paper
“This framing aligns well with how production debugging systems are used in practice, through writing discriminating query, refine the candidate set, and then write the next query [78], [61], [53], [54].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [78]
In the citing paper
“they face two key challenges: high computing overhead from template conversion via parsing algorithms like Drain, and real-time processing pressure from explosive log volume during fault concurrency, making processing efficiency the system bottleneck.”
From A Survey of Anomaly Detection and Root Cause Analysis Techniques for Artificial Intelligence for IT Operations (AIOps) in Large-Scale Software Systems (2026) · cites this paper as [9]
In the citing paper
“The industry widely adopts online clustering algorithms based on parse trees such as Drain [9] , which can compress data dimensions efficiently but has insufficient discrimination for templates with high semantic similarity.”
From A Survey of Anomaly Detection and Root Cause Analysis Techniques for Artificial Intelligence for IT Operations (AIOps) in Large-Scale Software Systems (2026) · cites this paper as [9]
In the citing paper
“While computationally lightweight, these methods struggle to adapt to evolving workloads and often suffer from high false-positive rates when applied to complex, real-world clusters.”
From LINEADAPTER: Parameter-Efficient Fine-Tuning for Log Anomaly Detection and Root Cause Analysis (2025) · cites this paper as [4]
In the citing paper
“This frequencyratio-based heuristic has been widely adopted in prior work on log anomaly detection, particularly in template blacklisting and rule construction [4], [5].”
From LINEADAPTER: Parameter-Efficient Fine-Tuning for Log Anomaly Detection and Root Cause Analysis (2025) · cites this paper as [4]
In the citing paper
“It uses the Drain algorithm for parsing logs in real time, showing superior performance across benchmarks like HDFS and Thunderbird [19].”
From Log anomaly detection in AIOps: A real-world implementation using Large Language Models (2026) · cites this paper as [19]
Citations
Cites
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Term-Weighting Approaches in Automatic Text Retrieval
Salton, G., & Buckley, C. (1988) · Information Processing & Management · doi:10.1016/0306-4573(88)90021-0 · Cited by 11,421
Introduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288
Detecting large-scale system problems by mining console logs
Xu, W., Huang, L., Fox, A., Patterson, D., & Jordan, M. I. (2009) · Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles · doi:10.1145/1629575.1629587 · Cited by 1,332
What Supercomputers Say: A Study of Five System Logs
Oliner, A. J., & Stearley, J. (2007) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2007.103 · Cited by 695
Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis
Fu, Q., Lou, J., Wang, Y., & Jiang, L. (2009) · Proceedings · doi:10.1109/icdm.2009.60 · Cited by 555
Spell: Streaming Parsing of System Event Logs
Du, M., & Li, F. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0103 · Cited by 423
An Evaluation Study on Log Parsing and Its Use in Log Mining
He, P., Zhu, J., He, S., Li, J., & Lyu, M. R.-T. (2016) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2016.66 · Cited by 274
Self-citationLogSig: generating system events from raw textual logs
Tang, L., Li, T., & Perng, C.-S. (2011) · Proceedings of the 20th ACM international conference on Information and knowledge management · doi:10.1145/2063576.2063690 · Cited by 244
Learning to Log: Helping Developers Make Informed Logging Decisions
Zhu, J., He, P., Fu, Q., Zhang, H., Lyu, M. R.-T., & Zhang, D. (2015) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2015.60 · Cited by 229
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Items 601–700 of 985
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Wang, P., Zhang, X., & Cao, Z. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-024-06850-y · Cited by 2
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Wu, X., Li, H., & Khomh, F. (2025) · Automated Software Engineering · doi:10.1007/s10515-025-00527-3 · Cited by 2
GSTGPT: A GPT-Based Framework for Multi-Source Data Anomaly Detection
Liu, J., Fang, M., Zhang, S., Shan, F., & Li, J. (2025) · Information · doi:10.3390/info16110959 · Cited by 1
CSLParser: A Collaborative Framework Using Small and Large Language Models for Log Parsing
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LogMoE: Lightweight Expert Mixture for Cross-System Log Anomaly Detection
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Zhou, Y., Shen, Y., & Li, B. (2025) · Asia-Pacific Software Engineering Conference (APSEC) · doi:10.1109/apsec66846.2025.00016 · Cited by 1
Patel, T., Bayyavarapu, S. R. K. V., Soni, V. R., Purushothaman, R., Thokala, G. B., & Ranganathan, V. (2026) · International Conference on Advances in Artificial Intelligence and Machine Learning (AAIML) · doi:10.1109/aaiml67890.2026.11498111 · Cited by 1
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Developing a Taxonomy for Advanced Log Parsing Techniques
Sedki, I., Hamou‐Lhadj, A., Mohamed, O. A., & Ezzati‐Jivan, N. (2025) · IEEE International Conference on Program Comprehension (ICPC) · doi:10.1109/icpc66645.2025.00061 · Cited by 1
AAR-Log: A robust log anomaly detection method resisting adversarial attacks
Wu, J., Zhang, S., Liu, H., & Wang, Y. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111471 · Cited by 1
Liu, Z., & Liao, N. (2025) · International Conference on Electronic Engineering and Information Systems (EEISS) · doi:10.1109/eeiss65394.2025.11085929 · Cited by 1
E-Log: Fine-Grained Elastic Log-Based Anomaly Detection and Diagnosis for Databases
Zhang, L., Jia, T., Tan, X., Huang, X., Jia, M., Liu, H., Wu, Z., & Li, Y. (2025) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2025.3594870 · Cited by 4
Sun, Y., Keung, J., Yu, H. K., Liu, S., Liao, Y., & Zhang, J. (2025) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac65507.2025.00173 · Cited by 1
Predictive Risk Analytics for Early-Warning Systems in Enterprise Technology Estates
Okwesa, I. J., Ashore-Onisemo, F., Oyesiji, S. O., & Abdulmalik, A. U. (2024) · Shodhshauryam International Scientific Refereed Research Journal · doi:10.32628/shisrrj24797 · Cited by 2
Research on network cloud equipment anomaly and root cause analysis
Zou, D., Ding, J., Wang, X., Ye, X., & Ouyang, Y. (2022) · ITU Journal on Future and Evolving Technologies · doi:10.52953/tvlo2995 · Cited by 1
A Heuristic Approach Using Template Miners for Error Prediction in Telecommunication Networks
Marjai, P., Lehotay–Kéry, P., & Kiss, A. (2022) · IEEE Access · doi:10.1109/access.2022.3221427 · Cited by 2
Chen, P., Chao, G., Li, Y., He, H., Lei, H., Li, M., Gao, D., & Guo, S. (2023) · IEEE International Conference on Image Processing and Computer Applications (ICIPCA) · doi:10.1109/icipca59209.2023.10257676 · Cited by 1
Discovering relationships between data in enterprise system using log analysis
Korzeniowski, Ł., & Goczyła, K. (2023) · Annals of Computer Science and Information Systems · doi:10.15439/2023f4617 · Cited by 1
A Distributed Storage System for System Logs Based on Hybrid Compression Scheme
Chang, B., Zhou, F., Wang, Z., Wen, Y., & Zhang, B. (2023) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-socialcom-sustaincom59178.2023.00127 · Cited by 1
Automation of Log Collection and Incident Triage for Security Operations: A Review
Okwesa, I. J., Ajiga, D., & Iwuanyanwu, U. A. (2019) · Iconic Research and Engineering Journals · doi:10.64388/irev3i5-1722629 · Cited by 1
Effective Removal of Operational Log Messages: an Application to Model InferencePreprint
Shin, D., Bianculli, D., & Briand, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.07194 · Cited by 1
Deep learning based log anomaly detection with time differences
Sağında, B. (2020) · Cited by 1
Log Summarisation for Defect Evolution Analysis
Dolga, R., Zmigrod, R., Silva, R., Alamir, S., & Shah, S. (2023) · doi:10.1145/3617572.3617881 · Cited by 1
Lin, Y., Loo, T.-H., Yudha, F., Lai, Y., & Hwang, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5143339 · Cited by 1
Yu, E. (2026) · 2026 6th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) · doi:10.1109/aiita69518.2026.11567316
LINEADAPTER: Parameter-Efficient Fine-Tuning for Log Anomaly Detection and Root Cause Analysis
Liu, X., Bao, W., Zhang, Y., Li, Y., Ranjan, R., & Jha, D. N. (2025) · 2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11322907
Explainable Anomaly Detection for System Logs in Distributed Environments
Gu, Z., Yue, W., & Liu, C. (2026) · Computers, Materials & Continua · doi:10.32604/cmc.2026.077388
“First, the Drain [42] framework is adopted to automatically generate log templates: it learns template structures through depth-first search, avoiding the limitations of traditional rule bases.”
LogRCA: Log-based Root Cause Analysis for Distributed Services
Wittkopp, T., Wiesner, P., & Kao, O. (2024) · arXiv · doi:10.48550/arxiv.2405.13599 · Cited by 16
“DeepLog [3] uses templates [8] and an LSTM.”
MethodLarge language models for agentic NetOps and AIOps: Architectures, evaluation, and safety
Bilal, M., Crowcroft, J., Wang, R., Xu, X., & Dustdar, S. (2027) · Computer Science Review · doi:10.1016/j.cosrev.2026.101075 · Cited by 10
Building an Adaptive Logs Classification System: Industrial Report
Rudakov, K., Novikov, A., Sitnikov, A., Tsymbalov, E., & Zverev, A. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-71472-7_15
Metadata
- Authors
- He, P., Zhu, J., Zheng, Z., & Lyu, M. R.
- Affiliations
- Chinese University of Hong Kong (HK)
- Sun Yat-sen University (CN)
- Venue
- 2017 IEEE International Conference on Web Services (ICWS)
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- 33-40
- OpenAlex
- W2754665629
- Cited by
- 943
- References
- 33
- Citations by year
- 2026: 164 · 2025: 184 · 2024: 184 · 2023: 157 · 2022: 84 · 2021: 91 · 2020: 52 · 2019: 18 · 2018: 7 · 2017: 2
- FWCI
- 17.22
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Web Services (ICWS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- log parsing, online parsing, log analysis, web service management, anomaly detection
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