{"id":13472151,"url":"https://github.com/logpai/awesome-log-analysis","last_synced_at":"2026-01-30T16:07:39.452Z","repository":{"id":44904113,"uuid":"124501318","full_name":"logpai/awesome-log-analysis","owner":"logpai","description":"A list of awesome research on log analysis, anomaly detection, fault localization, and AIOps","archived":false,"fork":false,"pushed_at":"2023-12-31T14:10:09.000Z","size":135,"stargazers_count":784,"open_issues_count":3,"forks_count":127,"subscribers_count":36,"default_branch":"master","last_synced_at":"2026-01-29T07:44:36.759Z","etag":null,"topics":["aiops","anamoly-detection","bug-finding","failure-diagnosis","fault-localization","log-analysis"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/logpai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2018-03-09T07:00:46.000Z","updated_at":"2026-01-21T04:41:53.000Z","dependencies_parsed_at":"2024-01-16T03:44:36.558Z","dependency_job_id":"db3dc9da-5b23-48f8-937b-058fbf5bb92b","html_url":"https://github.com/logpai/awesome-log-analysis","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/logpai/awesome-log-analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/logpai%2Fawesome-log-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/logpai%2Fawesome-log-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/logpai%2Fawesome-log-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/logpai%2Fawesome-log-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/logpai","download_url":"https://codeload.github.com/logpai/awesome-log-analysis/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/logpai%2Fawesome-log-analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28914961,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-30T12:13:43.263Z","status":"ssl_error","status_checked_at":"2026-01-30T12:13:22.389Z","response_time":66,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["aiops","anamoly-detection","bug-finding","failure-diagnosis","fault-localization","log-analysis"],"created_at":"2024-07-31T16:00:52.397Z","updated_at":"2026-01-30T16:07:39.435Z","avatar_url":"https://github.com/logpai.png","language":null,"funding_links":[],"categories":["Others","Table of Contents","Other Lists","miscellaneous","Master-Level","AI for *Ops"],"sub_categories":["TeX Lists","M.Sc.: Big Data and Cloud Computing for AI","Observability \u0026 Monitoring with AI"],"readme":"\u003cp align=\"center\"\u003e \u003ca href=\"https://github.com/logpai\"\u003e \u003cimg src=\"https://github.com/logpai/logpai.github.io/blob/master/img/logpai_logo.jpg\" width=\"425\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n# Awesome Log Analysis\nA curated list of awesome publications and researchers on log analysis, anomaly detection, fault localization, and AIOps.\n\n\n- [Awesome Log Analysis](#awesome-log-analysis)\n  - [Researchers](#researchers)\n  - [Conferences and Journals](#conferences-and-journals)\n  - [Datasets](#datasets)\n  - [Papers](#papers)\n    - [Surveys \u0026 Tutorials \u0026 Magazines](#surveys--tutorials--magazines)\n    - [Logging](#logging)\n    - [Log Compression](#log-compression)\n    - [Log Parsing](#log-parsing)\n    - [Log Mining](#log-mining)\n      - [Anomaly Detection](#anomaly-detection)\n      - [Failure Prediction](#failure-prediction)\n      - [Failure Diagnosis](#failure-diagnosis)\n      - [Others](#others)\n  - [License](#license)\n\n\n## Researchers\n| China (\u0026 HK SAR) | ||||\n| :---------| :------ | :------ | :------ | :------ |\n| [Michael R. Lyu](http://www.cse.cuhk.edu.hk/lyu/), CUHK | [Dongmei Zhang](https://www.microsoft.com/en-us/research/people/dongmeiz/), Microsoft | [Pengfei Chen](http://sdcs.sysu.edu.cn/content/3747), SYSU | [Dan Pei](https://netman.aiops.org/~peidan/), Tsinghua | |\n| [Pinjia He](https://pinjiahe.github.io/), CUHK-Shenzhen|\n| **USA** |||||\n| [Yuanyuan Zhou](https://cseweb.ucsd.edu/~yyzhou/), UCSD | [Tao Xie](http://taoxie.cs.illinois.edu/), UIUC | [Dawson Engler](http://web.stanford.edu/~engler/), Stanford | [Ben Liblit](http://pages.cs.wisc.edu/~liblit/#bug-isolation), Wisconsin–Madison ||\n| **Canada** |||||\n| [Ding Yuan](http://www.eecg.toronto.edu/~yuan/Home.html), Toronto University | [Ahmed E. Hassan](http://research.cs.queensu.ca/~ahmed/home/), Queen's University | [Weiyi Shang](https://users.encs.concordia.ca/~shang/), Concordia University |[Zhen Ming (Jack) Jiang](http://www.cse.yorku.ca/~zmjiang/), York University||\n| [Wahab Hamou-Lhadj](https://users.encs.concordia.ca/~abdelw/), Concordia University|\n| **UK** |||||\n|  |||||\n| **Europe** |||||\n|  |||||\n| **Australia** |||||\n| [Ingo Weber](https://people.csiro.au/W/I/Ingo-Weber), CSIRO |||||\n\n\n## Conferences and Journals\nLogs are a type of valuable data generated from many sources such as software, systems, networks, devices, etc. They have also been used for a number of tasks related to reliability, security, performance, and energy. Therefore, the research of log analysis has attracted interests from different research areas.\n\n+ **System area**\n    + Conferences: [OSDI](https://dblp.uni-trier.de/db/conf/osdi/index) | [SOSP](https://dblp.uni-trier.de/db/conf/sosp/index) | [ATC](https://dblp.uni-trier.de/db/conf/usenix/index) | [ICDCS](https://dblp.uni-trier.de/db/conf/icdcs/index)\n    + Journals: [TC](https://dblp.uni-trier.de/db/journals/tc/index.html) | [TOCS](https://dblp.uni-trier.de/db/journals/tocs/index) | [TPDS](https://dblp.uni-trier.de/db/journals/tpds/index.html)\n+ **Cloud computing area**\n    + Conferences: [SoCC](https://dblp.uni-trier.de/db/conf/cloud/index.html) | [CLOUD](https://dblp.uni-trier.de/db/conf/IEEEcloud/index)\n    + Journals: [TCC](https://dblp.uni-trier.de/db/journals/tcc/index.html)\n+ **Networking area**\n    + Conferences: [NSDI](https://dblp.uni-trier.de/db/conf/nsdi/index) | [INFOCOMM](https://dblp.uni-trier.de/db/conf/infocom/index)\n    + Journals: [TON](https://dblp.uni-trier.de/db/journals/ton/index.html)\n+ **Software engineering area**\n    + Conferences: [ICSE](https://dblp.uni-trier.de/db/conf/icse/index) | [FSE](https://dblp.uni-trier.de/db/conf/sigsoft/index) | [ASE](https://dblp.org/db/conf/kbse/index.html)\n    + Journals: [TSE](https://dblp.org/db/journals/tse/index) | [TOSEM](https://dblp.uni-trier.de/db/journals/tosem/index)\n+ **Reliability area**\n    + Conferences: [DSN](https://dblp.uni-trier.de/db/conf/dsn/index) | [ISSRE](https://dblp.uni-trier.de/db/conf/issre/index.html) | [SRDS](https://dblp.uni-trier.de/db/conf/srds/index)\n    + Journals: [TDSC](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8858) | [TR](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=24)\n+ **Security area**\n    + Conferences: [CCS](http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=83847) | [DSN](http://www.dsn.org/)\n    + Journals: [TDSC](https://dblp.uni-trier.de/db/journals/tdsc/index.html)\n+ **AI and Bigdata area**\n    + Conferences: [KDD](https://dblp.uni-trier.de/db/conf/kdd/index) | [CIKM](https://dblp.uni-trier.de/db/conf/cikm/index) | [ICDM](https://dblp.uni-trier.de/db/conf/icdm/index) | [BigData](https://dblp.org/db/conf/bigdataconf/index)\n    + Journals: [TKDE](https://dblp.uni-trier.de/db/journals/tkde/index) | [TBD](https://dblp.uni-trier.de/db/journals/tbd/index.html)\n+ **Industrial conferences**\n    + [SREcon](https://www.usenix.org/conferences/byname/925) | [GOPS](https://www.bagevent.com/event/GOPS2019-shenzhen?bag_track=bagevent)\n\n\n## Datasets\nLoghub\n\n\n## Papers\n\n### Surveys \u0026 Tutorials \u0026 Magazines\n1. [**ACM Computing Survey**] [A Survey on Automated Log Analysis for Reliability Engineering](https://arxiv.org/abs/2009.07237)\n1. [**Blog**] [What is AIOps? Artificial Intelligence for IT Operations Explained](http://www.bmc.com/blogs/what-is-aiops/)\n1. [**Book'14**] [I Heart Logs](https://www.oreilly.com/library/view/i-heart-logs/9781491909379/)\n1. [**Book'12**] [Logging and Log Management: The Authoritative Guide to Understanding the Concepts Surrounding Logging and Log Management](http://mirror.thelifeofkenneth.com/sites/qt.vidyagam.es/library/Forensics/Logging%20and%20Log%20Management_%20The%20Authoritats%20Surrounding%20Logging%20and%20Log%20Management/Logging%20and%20Log%20Management_%20The%20Authoritative%20Guide%20to%20Undeanagement%20-%20Anton%20Chuvakin%20\u0026%20Kevin%20Schmidt%20\u0026%20Chris%20Phillips.pdf), by Anton A. Chuvakin, Kevin J. Schmidt, Christopher Phillips.\n1. [**Thesis**] [Log Engineering: Towards Systematic Log Mining to Support the Development of Ultra-large Scale Systems](https://users.encs.concordia.ca/~shang/pubs/2014_LogEngineering_TowardsSystematicLogMiningToSupportTheDevelopmentOfUltra-largeScaleSystems.pdf)\n1. [**IST'20**] [A Systematic Literature Review on Automated Log Abstraction Techniques](https://www.sciencedirect.com/science/article/pii/S0950584920300264)\n1. [**IEEE Software'16**] [Operational-Log Analysis for Big Data Systems: Challenges and Solutions](https://www.computer.org/csdl/magazine/so/2016/02/mso2016020052/13rRUzp02mr)\n\n\n### Logging\n- [OSDI 2012] [Be Conservative: Enhancing Failure Diagnosis with Proactive Logging](https://www.eecg.utoronto.ca/~yuan/papers/osdi12-errlog.pdf)\n- [TSE 2013] [Event Logs for the Analysis of Software Failures: A Rule-Based Approach](http://ieeexplore.ieee.org/document/6320555/)\n- [ICSE 2015] [Learning to Log: Helping Developers Make Informed Logging Decisions](http://ieeexplore.ieee.org/document/7194593/)\n- [ICSE 2015] [Where do developers log? an empirical study on logging practices in industry](http://dl.acm.org/citation.cfm?doid=2591062.2591175)\n- [ATC 2015] [Log2 : A Cost-Aware Logging Mechanism for Performance Diagnosis](https://www.usenix.org/system/files/conference/atc15/atc15-paper-ding.pdf)\n- [SOSP 2017] [Log20: Fully Automated Optimal Placement of Log Printing Statements under Specified Overhead Threshold](http://log20.dsrg.utoronto.ca/log20_sosp17_paper.pdf)\n- [HotOS 2017] [The Game of Twenty Questions: Do You Know Where to Log?](https://dl.acm.org/doi/10.1145/3102980.3103001)\n- [ASE 2020] [Where Shall We Log? Studying and Suggesting Logging Locations in Code Blocks](https://users.encs.concordia.ca/~shang/pubs/Zhenhao_ASE20.pdf)\n- [ASPLOS 2011] [Improving Software Diagnosability via Log Enhancement](http://opera.ucsd.edu/paper/asplos11-logenhancer.pdf)\n- [ASE 2018] [Characterizing the Natural Language Descriptions in Software Logging Statements](https://pinjiahe.github.io/papers/ASE18.pdf)\n- [TSE 2019] [Which Variables Should I Log?](https://xin-xia.github.io/publication/tse197.pdf)\n- [ICPC 2019] [PADLA: a dynamic log level adapter using online phase detection](https://sel.ist.osaka-u.ac.jp/lab-db/betuzuri/archive/1157/1157.pdf)\n- [ECOOP 1997] [Aspect-oriented programming](https://www.cs.ubc.ca/~gregor/papers/kiczales-ECOOP1997-AOP.pdf)\n- [DSN 2010] [Assessing and improving the effectiveness of logs for the analysis of software faults](http://ieeexplore.ieee.org/document/5544279/)\n- [ICSE 2012] [Characterizing logging practices in open-source software](http://petertsehsun.github.io/soen691/current/papers/log_icse12.pdf)\n- [ICSME 2014] [Understanding Log Lines Using Development Knowledge](http://ieeexplore.ieee.org/document/6976068/)\n- [ICSE 2015] [Industry practices and event logging: assessment of a critical software development process](https://dl.acm.org/doi/10.5555/2819009.2819035)\n- [ESE 2015] [Studying the relationship between logging characteristics and the code quality of platform software](http://link.springer.com/10.1007/s10664-013-9274-8)\n- [ICSE 2017] [Characterizing and Detecting Anti-patterns in the Logging Code](https://dl.acm.org/doi/pdf/10.1109/ICSE.2017.15)\n- [OSDI 2018] [The FuzzyLog: A Partially Ordered Shared Log](https://www.usenix.org/conference/osdi18/presentation/lockerman)\n- [ATC 2018] [Troubleshooting Transiently-Recurring Errors in Production Systems with Blame-Proportional Logging](https://www.usenix.org/system/files/conference/atc18/atc18-luo.pdf)\n- [ATC 2018] [NanoLog: A Nanosecond Scale Logging System](https://www.usenix.org/system/files/conference/atc18/atc18-yang.pdf)\n- [NSDI 2018] [Carousel: Scalable Logging for Intrusion Prevention Systems](https://www.usenix.org/conference/nsdi10-0/carousel-scalable-logging-intrusion-prevention-systems)\n- [ICSE 2019] [DLFinder: Characterizing and Detecting Duplicate Logging Code Smells](https://users.encs.concordia.ca/~shang/pubs/icse2019_zhenhao.pdf)\n- [ICSE 2016] [The bones of the system: a case study of logging and telemetry at Microsoft](http://dl.acm.org/citation.cfm?doid=2889160.2889231)\n- [MSR 2016] [Logging library migrations: a case study for the apache software foundation projects](http://dl.acm.org/citation.cfm?doid=2901739.2901769)\n- [ESE 2017] [Characterizing logging practices in Java-based open source software projects - a replication study in Apache Software Foundation](http://link.springer.com/10.1007/s10664-016-9429-5)\n- [ESE 2018] [Studying and detecting log-related issues](http://link.springer.com/10.1007/s10664-018-9603-z)\n- [ESE 2018] [Examining the stability of logging statements](http://link.springer.com/10.1007/s10664-017-9518-0)\n- [ESE 2018] [An exploratory study on assessing the energy impact of logging on Android applications](https://www.eecs.yorku.ca/~zmjiang/publications/emse2017_chowdhury.pdf)\n- [ESE 2019] [Studying the characteristics of logging practices in mobile apps: a case study on F-Droid](http://link.springer.com/10.1007/s10664-019-09687-9)\n- [ICSE 2020] [Studying the Use of Java Logging Utilities in the Wild](http://www.cse.yorku.ca/~zmjiang/publications/icse2020_chen.pdf)\n- [EMSE 2022] [The Sense of Logging in the Linux Kernel](https://users.encs.concordia.ca/~abdelw/papers//EMSE22_LinuxLogs.pdf)\n\n\n### Log Compression\n- [IPDPS 2006] [Lossless compression for large scale cluster logs](https://ieeexplore.ieee.org/document/1639692)\n- [ADBIS 2007] [Fast and efficient log file compression](http://www.adbis.org/docs/lp/6.pdf)\n- [ICSE 2008] [An Industrial Case Study of Customizing Operational Profiles Using Log Compression](https://dl.acm.org/doi/abs/10.1145/1368088.1379445)\n- [SIGMOD 2013] [Adaptive log compression for massive log data](https://dl.acm.org/doi/10.1145/2463676.2465341)\n- [IEEE Trustcom/BigDataSE/ISPA 2016] [MLC: An Efficient Multi-level Log Compression Method for Cloud Backup Systems](https://ieeexplore.ieee.org/document/7847098/)\n- [TCSET 2008] [Sub-atomic field processing for improved web log compression](https://ieeexplore.ieee.org/document/5423436)\n- [CCGRID 2015] [Cowic: A column-wise independent compression for log stream analysis](https://ieeexplore.ieee.org/document/7152468)\n- [IMCC 2014] [Lightweight Packing of Log Files for Improved Compression in Mobile Tactical Networks](https://ieeexplore.ieee.org/document/6956758)\n- [DCC 2004] [High density compression of log files](https://ieeexplore.ieee.org/document/1281533)\n- [DaWaK 2003] [Comprehensive Log Compression with Frequent Patterns](https://link.springer.com/chapter/10.1007/978-3-540-45228-7_36)\n- [ICEIS 2019] [Rough Logs: A Data Reduction Approach for Log Files](https://www.scitepress.org/Papers/2019/77351/pdf/index.html)\n- [ASE 2019] [Logzip: extracting hidden structures via iterative clustering for log compression](https://arxiv.org/abs/1910.00409)\n- [EMSE 2019] [A Study of the Performance of General Compressors on Log Files](https://users.encs.concordia.ca/~shang/pubs/Kundi_EMSE2020.pdf)\n- [Ph.D. Dissertation 2008] [Using semantic knowledge to improve compression on log files](https://homes.cs.ru.ac.za/B.Irwin/theses/Otten%202008%20%20Msc%20Using%20semantic%20knowledge%20to%20improve%20compression%20on%20log%20files.pdf)\n\n\n### Log Parsing\n- [IPOM'03] [A Data Clustering Algorithm for Mining Patterns from Event Logs](http://www.quretec.com/u/vilo/edu/2003-04/DM_seminar_2003_II/ver1/P12/slct-ipom03-web.pdf)\n- [QSIC'08] [Abstracting Execution Logs to Execution Events for Enterprise Applications](https://www.researchgate.net/publication/4366728_Abstracting_Execution_Logs_to_Execution_Events_for_Enterprise_Applications_Short_Paper)\n- [ICDM'09] [Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/DM790-CR.pdf)\n- [MSR'10] [Abstracting Log Lines to Log Event Types for Mining Software System Logs](http://www.se.rit.edu/~mei/publications/pdfs/Abstracting-Log-Lines-to-Log-Event-Types-for-Mining-Software-System-Logs.pdf)\n- [CIKM'11] [LogSig: Generating System Events from Raw Textual Logs](https://users.cs.fiu.edu/~taoli/pub/liang-cikm2011.pdf) \n- [KDD'09] [Clustering Event Logs Using Iterative Partitioning](https://web.cs.dal.ca/~makanju/publications/paper/kdd09.pdf)\n- [TKDE'12] [A Lightweight Algorithm for Message Type Extraction in System Application Logs](https://ieeexplore.ieee.org/document/5936060)\n- [CNSM'15] [LogCluster - A Data Clustering and Pattern Mining Algorithm for Event Logs](http://dl.ifip.org/db/conf/cnsm/cnsm2015/1570161213.pdf)\n- [CIKM'16] [LogMine: Fast Pattern Recognition for Log Analytics](http://www.cs.unm.edu/~mueen/Papers/LogMine.pdf)\n- [TDSC'18] [Towards Automated Log Parsing for Large-Scale Log Data Analysis](https://pinjiahe.github.io/papers/TDSC17.pdf)\n- [ICPC'18] [A Search-based Approach for Accurate Identification of Log Message Formats](http://publications.uni.lu/bitstream/10993/35286/1/ICPC-2018.pdf)\n- [SCC'13] [Incremental Mining of System Log Format](http://ieeexplore.ieee.org/document/6649746/)\n- [arXiv'15] [Length Matters: Clustering System Log Messages using Length of Words](https://arxiv.org/pdf/1611.03213.pdf)\n- [TKDE'18] [Spell: Online Streaming Parsing of Large Unstructured System Logs](https://ieeexplore.ieee.org/document/8489912)\n- [ICWS'17] [Drain: An Online Log Parsing Approach with Fixed Depth Tree](https://jiemingzhu.github.io/pub/pjhe_icws2017.pdf)\n- [arXiv'18] [A Directed Acyclic Graph Approach to Online Log Parsing](https://arxiv.org/abs/1806.04356)\n- [TSE'20] [Logram: Efficient Log Parsing Using n-Gram Dictionaries](https://arxiv.org/abs/2001.03038)\n- [ICSE-SEIP'19] [Tools and benchmarks for automated log parsing](https://arxiv.org/abs/1811.03509)\n- [ICSME'22] [An Effective Approach for Parsing Large Log Files](https://users.encs.concordia.ca/~abdelw/papers/ICSME2022_ULP.pdf)\n\n\n\n### Log Mining\n\n#### Anomaly Detection\n- [OSDI 2016] [Non-intrusive performance profiling for entire software stacks based on the flow reconstruction principle](https://www.usenix.org/system/files/conference/osdi16/osdi16-zhao.pdf)\n- [FSE 2018] [Using finite-state models for log differencing](https://www.cs.tau.ac.il/~maozs/papers/log-diff-fse18.pdf)\n- [ICSE 2016] [Behavioral log analysis with statistical guarantees](https://www.cs.tau.ac.il/~maozs/papers/sg-icse16.pdf#:~:text=Behavioral%20Log%20Analysis%20with%20Statistical%20Guarantees%20Nimrod%20Busany,temporal%20properties%20from%20logs%20generated%20by%20run-ning%20systems.)\n- [FSE 2011] [Leveraging existing instrumentation to automatically infer invariant-constrained models](https://www.cs.ubc.ca/~bestchai/papers/esecfse2011-final.pdf)\n- [KDD 2010] [Mining program workflow from interleaved traces](https://dl.acm.org/doi/10.1145/1835804.1835883)\n- [ICSE 2014] [Inferring models of concurrent systems from logs of their behavior with CSight](https://dl.acm.org/doi/10.1145/2568225.2568246)\n- [ASE 2019] [Statistical log differencing](http://www.mysmu.edu/faculty/davidlo/papers/ase19-sld.pdf)\n- [SOSP 2009] [Detecting Large-Scale System Problems by Mining Console Logs](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-103.pdf)\n- [IPOM 2003] [A data clustering algorithm for mining patterns from event logs](https://ristov.github.io/publications/slct-ipom03-web.pdf)\n- [FSE 2018] [Identifying impactful service system problems via log analysis](https://shilinhe.github.io/media/papers/fse18.pdf)\n- [ICSE 2016] [Log clustering based problem identification for online service systems](https://dl.acm.org/doi/pdf/10.1145/2889160.2889232)\n- [ICDM 2007] [Failure prediction in ibm bluegene/l event logs](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=4470294)\n- [IEICE Transactions on Communications 2018] [Proactive failure detection learning generation patterns of large-scale network logs](https://dl.acm.org/doi/10.1109/CNSM.2015.7367332)\n- [ISSRE 2015] [Experience report: Anomaly detection of cloud application operations using log and cloud metric correlation analysis](https://ieeexplore.ieee.org/document/7381796)\n- [USENIX ATC 2010] [Mining Invariants from Console Logs for System Problem Detection](https://dl.acm.org/doi/10.5555/1855840.1855864)\n- [ICSE 2013] [Assisting developers of big data analytics applications when deploying on hadoop clouds](http://www.cse.yorku.ca/~zmjiang/publications/ICSE2013_Shang.pdf)\n- [ICDM 2009] [Online system problem detection by mining patterns of console logs](https://people.eecs.berkeley.edu/~jordan/papers/xu-etal-icdm09.pdf)\n- [ISSRE 2017] [Experience report: Log-based behavioral differencing](https://ieeexplore.ieee.org/document/8109094)\n- [KDD 2016] [Anomaly detection using program control flow graph mining from execution logs](https://www.kdd.org/kdd2016/papers/files/adf1233-nandiA.pdf)\n- [ICDM 2009] [Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/DM790-CR.pdf)\n- [ASPLOS 2016] [Cloudseer: Workflow monitoring of cloud infrastructures via interleaved logs](https://people.engr.ncsu.edu/gjin2/asplos-2016-cloudseer.pdf)\n- [KDD 2005] [Dynamic syslog mining for network failure monitoring](https://dl.acm.org/doi/10.1145/1081870.1081927)\n- [ISSRE 2016] [Experience report: System log analysis for anomaly detection](https://ieeexplore.ieee.org/document/7774521/)\n- [CCS 2017] [Deeplog: Anomaly detection and diagnosis from system logs through deep learning](https://www.cs.utah.edu/~lifeifei/papers/deeplog.pdf)\n- [FSE 2019] [Robust log-based anomaly detection on unstable log data](https://dl.acm.org/doi/pdf/10.1145/3338906.3338931)\n- [IJCAI 2019] [LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs](https://www.ijcai.org/Proceedings/2019/0658.pdf)\n- [ICCCN 2020] [Semantic-aware Representation Framework for Online Log Analysis](http://nkcs.iops.ai/wp-content/uploads/2020/05/paper-ICCCN20-Log2Vec.pdf)\n- [TCCN 2020] [An Intelligent Anomaly Detection Scheme for Micro-services Architectures with Temporal and Spatial Data Analysis](https://ieeexplore.ieee.org/document/8957683)\n- [ISSRE 2020] [Cross-System Log Anomaly Detection for Software Systems (to appear)]\n- [Information Systems Frontiers 2020] [LogGAN: a Log-level Generative Adversarial Network for Anomaly Detection using Permutation Event Modeling](https://link.springer.com/article/10.1007/s10796-020-10026-3)\n- [DASC/PiCom/DataCom/CyberSciTech 2018] [Detecting anomaly in big data system logs using convolutional neural network](https://ieeexplore.ieee.org/document/8511880)\n- [CCS 2019] [Log2vec: A Heterogeneous Graph Embedding Based Approach for Detecting Cyber Threats within Enterprise](https://dl.acm.org/doi/10.1145/3319535.3363224)\n- [MLCS 2018] [Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection](https://dl.acm.org/doi/pdf/10.1145/3217871.3217872)\n\n\n#### Failure Prediction\n- [MACS18] [PreFix: Switch failure prediction in datacenter networks](https://doi.org/10.1145/3179405)\n- [HPDC18] [Desh: deep learning for system health prediction of lead times to failure in HPC](https://doi.org/10.1145/3208040.3208051)\n- [KDD03] [Critical event prediction for proactive management in large-scale computer clusters](https://doi.org/10.1145/956750.956799)\n- [IPDPS20] [Aarohi: Making real-time node failure prediction feasible](https://doi.org/10.1109/IPDPS47924.2020.00115)\n- [CLUSTER17] [Data Mining-Based Analysis of HPC Center Operations](https://doi.org/10.1109/CLUSTER.2017.23)\n- [CLUSTER14] [Exploring void search for fault detection on extreme scale systems](https://doi.org/10.1109/CLUSTER.2014.6968757)\n- [WWW19] [Outage Prediction and Diagnosis for Cloud Service Systems](http://dl.acm.org/citation.cfm?doid=3308558.3313501)\n- [FSE18] [Predicting Node failure in cloud service systems](http://dl.acm.org/citation.cfm?doid=3236024.3236060)\n- [FSE19] [Latent error prediction and fault localization for microservice applications by learning from system trace logs](http://dl.acm.org/citation.cfm?doid=3338906.3338961)\n\n#### Failure Diagnosis\n- [ICSE 2019] [An empirical study on leveraging logs for debugging production failures](https://dl.acm.org/doi/10.1109/ICSE-Companion.2019.00055)\n- [ASPLOS 2016] [SherLog: error diagnosis by connecting clues from run-time logs](http://opera.ucsd.edu/paper/asplos10-sherlog.pdf)\n- [ISSTA 2009] [AVA:automated interpretation of dynamically detected anomalies](https://dl.acm.org/doi/pdf/10.1145/1572272.1572300)\n- [IC2E 2016] [LOGAN: Problem diagnosis in the cloud using log-based reference models](https://ieeexplore.ieee.org/document/7484164)\n- [ICWS 2017] [An approach for anomaly diagnosis based on hybrid graph model with logs for distributed services](https://ieeexplore.ieee.org/document/8029741)\n- [Cloud 2017] [Logsed: Anomaly diagnosis through mining time-weighted control flow graph in logs](https://ieeexplore.ieee.org/document/8030620) \n- [FSE 2018] [CloudRaid: hunting concurrency bugs in the cloud via log-mining](https://dl.acm.org/doi/abs/10.1145/3236024.3236071)\n- [TPDS 2013] [Toward fine-grained, unsupervised, scalable performance diagnosis for production cloud computing systems](https://ieeexplore.ieee.org/document/6410318)\n- [CLUSTER 2014] [Digging deeper into cluster system logs for failure prediction and root cause diagnosis](https://ieeexplore.ieee.org/document/6968768)\n- [ASPLOS 2014] [Comprehending performance from real-world execution traces: A device-driver case](https://dl.acm.org/doi/10.1145/2644865.2541968)\n- [ICWS 2017] [Log-based abnormal task detection and root cause analysis for spark](https://ieeexplore.ieee.org/document/8029786)\n- [EDCC 2015] [Insights into the diagnosis of system failures from cluster message logs](https://ieeexplore.ieee.org/abstract/document/7371970)\n- [HPC 2010] [Diagnosing the root-causes of failures from cluster log files](https://ieeexplore.ieee.org/document/5713159)\n- [ASE 2019] [SCMiner: localizing system-level concurrency faults from large system call traces](https://ieeexplore.ieee.org/document/8952396)\n- [NSDI 2012] [Structured comparative analysis of systems logs to diag- nose performance problems](https://www.usenix.org/system/files/conference/nsdi12/nsdi12-final61.pdf)\n- [ICSE 2013] [Assisting developers of big data analytics applications when deploying on hadoop clouds](https://ieeexplore.ieee.org/document/6606586)\n- [TPDS 2016] [Failure diagnosis for distributed systems using targeted fault injection](https://ieeexplore.ieee.org/document/7484300)\n- [ICSE 2017] [What causes my test alarm? Automatic cause analysis for test alarms in system and integration testing](https://dl.acm.org/doi/10.1109/ICSE.2017.71)\n- [GLOBECOM 2018] [Root-Cause Diagnosis Using Logs Generated by User Actions](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8647957)\n- [ICSE 2019] [Mining Historical Issue Repositories to Heal Large-Scale Online Service Systems](https://ieeexplore.ieee.org/document/6903589)\n- [CLOUD 2019] [An Approach to Cloud Execution Failure Diagnosis Based on Exception Logs in OpenStack](https://ieeexplore.ieee.org/abstract/document/8814553)\n- [FAST 2009] [Understanding customer problem troubleshooting from storage system logs](https://www.usenix.org/legacy/events/fast09/tech/full_papers/jiang/jiang.pdf)\n- [DSN 2013] [Reading between the lines of failure logs: Understanding how HPC systems fail](https://ieeexplore.ieee.org/document/6575356)\n- [DSN 2014] [What logs should you look at when an application fails? insights from an industrial case study](https://ieeexplore.ieee.org/document/6903626)\n- [TSE 2018] [Fault analysis and debugging of microservice systems: Industrial survey, benchmark system, and empirical study](https://ieeexplore.ieee.org/document/8580420)\n- [FSE 2019] [How bad can a bug get? an empirical analysis of software failures in the OpenStack cloud computing platform](https://dl.acm.org/doi/10.1145/3338906.3338916)\n\n\n#### Others\n\n- [DSN14] [Mining Historical Issue Repositories to Heal Large-Scale Online Service Systems](https://doi.org/10.1109/DSN.2014.39)\n- [ASE98] [Testing using log file analysis: Tools, methods, and issues](https://doi.org/10.1109/ASE.1998.732614)\n- [ASE18] [An automated approach to estimating code coverage measures via execution logs](https://doi.org/10.1145/3238147.3238214)\n- [ASE19] [An experience report of generating load tests using log-recovered workloads at varying granularities of user behaviour](https://doi.org/10.1109/ASE.2019.00068)\n- [ASE15] [Have we seen enough traces? (T)](https://doi.org/10.1109/ASE.2015.62)\n- [ICSE08] [An approach to detecting duplicate bug reports using natural language and execution information](https://doi.org/10.1145/1368088.1368151)\n\n\n## License\nThis repo is under the MIT license.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flogpai%2Fawesome-log-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flogpai%2Fawesome-log-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flogpai%2Fawesome-log-analysis/lists"}