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Logs, being run-time information automatically generated by software, record system events and activities with their timestamps.
J. Cohen, “A coefficient of agreement for nominal scales,” Educational and psychological measurement , vol. 20, pp. 37–46, 1960. [Online]. Available: https://w3.ric.edu/faculty/organic/coge/cohen1960.pdf
1960
Earlier work this paper cites.
R. Caruana, “Multitask learning,” Machine learning , vol. 28, no. 1, pp. 41–75, 1997. [Online]. Available: https://doi.org/10.1023/A:10073 79606734
1997
Earlier work this paper cites.
2001
Earlier work this paper cites.
R. Vaarandi, “A data clustering algorithm for mining patterns from event logs,” in Proceedings of the 3rd IEEE Workshop on IP Operations & Management (IEEE Cat. No. 03EX764), Kansas City, MO, USA, Oct 3, 2003 . IEEE, 2003, pp. 119–126. [Online]. Available: https://ieeexplore.ieee.org/document/1251233
2003
Earlier work this paper cites.
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of machine Learning research , vol. 3, no. Jan, pp. 993–1022, 2003
2003
Earlier work this paper cites.
M. Chen, A. X. Zheng, J. Lloyd, M. I. Jordan, and E. Brewer, “Failure diagnosis using decision trees,” in International Conference on Autonomic Computing, New York, NY, USA, May 17-19, 2004 . IEEE Computer Society, 2004, pp. 36–43. [Online]. Available: http://doi.ieeecomputersociety.org/10.1109/ICAC.2004.31
2004
Earlier work this paper cites.
2008
Earlier work this paper cites.
Z. M. Jiang, A. E. Hassan, P. Flora, and G. Hamann, “Abstracting execution logs to execution events for enterprise applications (short paper),” in Proceedings of the Eighth International Conference on Quality Software, Oxford, UK, August 12-13, 2008, . IEEE Computer Society, 2008, pp. 181–186. [Online]. Available: https://doi.org/10.1109/QSIC.2008.50
2008
Earlier work this paper cites.
Y. Liang, Y. Zhang, H. Xiong, and R. Sahoo, “Failure prediction in ibm bluegene/l event logs,” in International Symposium on Parallel and Distributed Processing, Miami, Florida USA, April 14-18, 2008 . IEEE, 2008, pp. 1–5. [Online]. Available: https://doi.org/10.1109/IPDPS.2008.4536397
2008
Earlier work this paper cites.
W. Xu, L. Huang, A. Fox, D. A. Patterson, and M. Jordan, “Large-scale system problems detection by mining console logs,” EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-103, Jul 2009. [Online]. Available: http://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-103.html
2009
Earlier work this paper cites.
Q. Fu, J.-G. Lou, Y. Wang, and J. Li, “Execution anomaly detection in distributed systems through unstructured log analysis,” in International Conference on Data Mining, Miami, Florida, USA, December 6-9, 2009 . IEEE Computer Society, 2009, pp. 149–158. [Online]. Available: https://doi.org/10.1109/ICDM.2009.60
2009
Earlier work this paper cites.
W. Xu, L. Huang, A. Fox, D. Patterson, and M. I. Jordan, “Detecting large-scale system problems by mining console logs,” in Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles , 2009, pp. 117–132
2009
Earlier work this paper cites.
A. A. Makanju, A. N. Zincir-Heywood, and E. E. Milios, “Clustering event logs using iterative partitioning,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009 . ACM, 2009, pp. 1255–1264. [Online]. Available: https://doi.org/10.1145/1557019.1557154
2009
Earlier work this paper cites.
M. Nagappan and M. A. Vouk, “Abstracting log lines to log event types for mining software system logs,” in Proceedings of the 7th International Working Conference on Mining Software Repositories, Cape Town, South Africa, May 2-3, 2010 , IEEE. IEEE Computer Society, 2010, pp. 114–117. [Online]. Available: https://doi.org/10.1109/MSR.2010.5463281
2010
Earlier work this paper cites.
R. Řehůřek and P. Sojka, “Software framework for topic modelling with large corpora,” in Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks, Valletta, Malta, May 22, 2010 . University of Malta, 2010, pp. 46–50. [Online]. Available: http://www.fi.muni.cz/usr/sojka/presentations/lrec2010-poster-rehurek-sojka.pdf
2010
Earlier work this paper cites.
L. Tang, T. Li, and C.-S. Perng, “Logsig: Generating system events from raw textual logs,” in Proceedings of the 20th Conference on Information and Knowledge Management, UK, October 24-28, 2011 . ACM, 2011, pp. 785–794. [Online]. Available: https://doi.org/10.1145/2063576.2063690
2011
Earlier work this paper cites.
M. Sundermeyer, R. Schlüter, and H. Ney, “Lstm neural networks for language modeling,” in Annual Conference of the International Speech Communication Association, Portland, Oregon, USA, September 9-13, 2012 . ISCA, 2012, pp. 194–197. [Online]. Available: http://www.isca-speech.org/archive/interspeech_2012/i12_0194.html
2012
Earlier work this paper cites.
M. Mizutani, “Incremental mining of system log format,” in International Conference on Services Computing, Santa Clara, CA, USA, June 28 - July 3, 2013 . IEEE Computer Society, 2013, pp. 595–602. [Online]. Available: https://doi.org/10.1109/SCC.2013.73
2013
Earlier work this paper cites.
A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, May 26-31, 2013 . IEEE, 2013, pp. 6645–6649. [Online]. Available: https://doi.org/10.1109/ICASSP.2013.6638947
2013
Cited alongside, same era.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Annual Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, USA, December 5-8, 2013 , 2013, pp. 3111–3119. [Online]. Available: https://proceedings.neurips.cc/paper/2013/hash/9aa42b3188ec039965f3 c4923ce901b-Abstract.html
2013
Cited alongside, same era.
2015
Cited alongside, same era.
S. Messaoudi, A. Panichella, D. Bianculli, L. Briand, and R. Sasnauskas, “A search-based approach for accurate identification of log message formats,” in Proceedings of the 26th Conference on Program Comprehension, Gothenburg, Sweden, May 27-28, 2018 . ACM, 2018, pp. 167–177. [Online]. Available: https://doi.org/10.1145/3196321.3196340
2018
Later among the works it cites.
S. Lu, X. Wei, Y. Li, and L. Wang, “Detecting anomaly in big data system logs using convolutional neural network,” in Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, Athens, Greece, August 12-15, 2018 . IEEE Computer Society, 2018, pp. 151–158. [Online]. Available: https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00037
2018
Later among the works it cites.
T. Osadchiy, I. Poliakov, P. Olivier, M. Rowland, and E. Foster, “Recommender system based on pairwise association rules,” Expert Systems with Applications , vol. 115, pp. 535–542, 2019. [Online]. Available: https://doi.org/10.1016/j.eswa.2018.07.077
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M. Xuezhe and H. H. Eduard, “End-to-end sequence labeling via bi-directional lstm-cnns-crf,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, August 7-12, 2016 . The Association for Computer Linguistics, 2016. [Online]. Available: https://doi.org/10.18653/v1/p16-1101
2016
Cited alongside, same era.
J. P. Chiu and E. Nichols, “Named entity recognition with bidirectional lstm-cnns,” Trans. Assoc. Comput. Linguistics , vol. 4, pp. 357–370, 2016. [Online]. Available: https://transacl.org/ojs/index.php/ tacl/article/view/792
2016
Cited alongside, same era.
Q. Lin, H. Zhang, J.-G. Lou, Y. Zhang, and X. Chen, “Log clustering based problem identification for online service systems,” in Proceedings of the 38th International Conference on Software Engineering, Austin, TX, USA, May 14-22, 2016 - Companion Volume . ACM, 2016, pp. 102–111. [Online]. Available: https://doi.org/10.1145/2889160.2889232
2016
Cited alongside, same era.
S. He, J. Zhu, P. He, and M. R. Lyu, “Experience report: System log analysis for anomaly detection,” in International Symposium on Software Reliability Engineering, Ottawa, ON, Canada, October 23-27, 2016 . IEEE Computer Society, 2016, pp. 207–218. [Online]. Available: https://doi.org/10.1109/ISSRE.2016.21
2016
Cited alongside, same era.
2016
Cited alongside, same era.
P. Covington, J. Adams, and E. Sargin, “Deep neural networks for youtube recommendations,” in Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, September 15-19, 2016 . ACM, 2016, pp. 191–198. [Online]. Available: https://doi.org/10.1145/2959100.2959190
2016
Cited alongside, same era.
P. He, J. Zhu, Z. Zheng, and M. R. Lyu, “Drain: An online log parsing approach with fixed depth tree,” in 2017 IEEE International Conference on Web Services, Honolulu, HI, USA, June 25-30, 2017 . IEEE, 2017, pp. 33–40. [Online]. Available: https://doi.org/10.1109/ICWS.2017.13
2017
Cited alongside, same era.
R. He, W. S. Lee, H. T. Ng, and D. Dahlmeier, “An unsupervised neural attention model for aspect extraction,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics , 2017, pp. 388–397
2017
Cited alongside, same era.
M. Du, F. Li, G. Zheng, and V. Srikumar, “Deeplog: Anomaly detection and diagnosis from system logs through deep learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA, October 30 - November 03, 2017 . ACM, 2017, pp. 1285–1298. [Online]. Available: https://doi.org/10.1145/3133956.3134015
2017
Cited alongside, same era.
2018
Later among the works it cites.
H. Amar, L. Bao, N. Busany, D. Lo, and S. Maoz, “Using finite-state models for log differencing,” in Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2018, pp. 49–59
2018
Later among the works it cites.
A. Amar and P. C. Rigby, “Mining historical test logs to predict bugs and localize faults in the test logs,” in Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings, Montreal, QC, Canada, May 25-31, 2019 . IEEE / ACM, 2019, pp. 140–151. [Online]. Available: https://doi.org/10.1109/ICSE.2019.00031
2019
Later among the works it cites.
X. Zhang, Y. Xu, Q. Lin, B. Qiao, H. Zhang, Y. Dang, C. Xie, X. Yang, Q. Cheng, Z. Li et al. , “Robust log-based anomaly detection on unstable log data,” in Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Tallinn, Estonia, August 26-30, 2019 . ACM, 2019, pp. 807–817. [Online]. Available: https://doi.org/10.1145/3338906.3338931
2019
Later among the works it cites.
D. Cotroneo, L. De Simone, P. Liguori, R. Natella, and N. Bidokhti, “How bad can a bug get? an empirical analysis of software failures in the openstack cloud computing platform,” in Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Tallinn, Estonia, August 26-30, 2019 . ACM, 2019, pp. 200–211. [Online]. Available: https://doi.org/10.1145/3338906.3338916
2019
Later among the works it cites.
J. Liu, J. Zhu, S. He, P. He, Z. Zheng, and M. R. Lyu, “Logzip: extracting hidden structures via iterative clustering for log compression,” in International Conference on Automated Software Engineering, San Diego, CA, USA, November 11-15, 2019 . IEEE, 2019, pp. 863–873. [Online]. Available: https://doi.org/10.1109/ASE.2019.00085
2019
Later among the works it cites.
J. Tabassum, M. Maddela, W. Xu, and A. Ritter, “Code and named entity recognition in stackoverflow,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, July 5-10, 2020 . Association for Computational Linguistics, 2020, pp. 4913–4926. [Online]. Available: https://doi.org/10.18653/v1/2020.acl-main.443
2020
Later among the works it cites.
S. Nedelkoski, J. Bogatinovski, A. Acker, J. Cardoso, and O. Kao, “Self-attentive classification-based anomaly detection in unstructured logs,” in International Conference on Data Mining, Sorrento, Italy, November 17-20, 2020 . IEEE, 2020, pp. 1196–1201. [Online]. Available: https://doi.org/10.1109/ICDM50108.2020.00148
2020
Later among the works it cites.
N. Zhao, H. Wang, Z. Li, X. Peng, G. Wang, Z. Pan, Y. Wu, Z. Feng, X. Wen, W. Zhang et al. , “An empirical investigation of practical log anomaly detection for online service systems,” in Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2021, pp. 1404–1415. [Online]. Available: https://dl.acm.org/doi/abs/10.1145/3468264.3473933
2021
Closest in time.
A. R. Chen, T.-H. P. Chen, and S. Wang, “Pathidea: Improving information retrieval-based bug localization by re-constructing execution paths using logs,” IEEE Transactions on Software Engineering , 2021
2021
Closest in time.
G. Chu, J. Wang, Q. Qi, H. Sun, S. Tao, and J. Liao, “Prefix-graph: A versatile log parsing approach merging prefix tree with probabilistic graph,” in 2021 IEEE 37th International Conference on Data Engineering . IEEE, 2021, pp. 2411–2422. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9458609
2021
Closest in time.
M. Shetty, C. Bansal, S. Kumar, N. Rao, N. Nagappan, and T. Zimmermann, “Neural knowledge extraction from cloud service incidents,” in 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice . IEEE, 2021, pp. 218–227
2021
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2021
Closest in time.
S. He, P. He, Z. Chen, T. Yang, Y. Su, and M. R. Lyu, “A survey on automated log analysis for reliability engineering,” ACM Computing Surveys (CSUR) , vol. 54, no. 6, pp. 1–37, 2021
2021
Closest in time.
H. Ott, J. Bogatinovski, A. Acker, S. Nedelkoski, and O. Kao, “Robust and transferable anomaly detection in log data using pre-trained language models,” in 2021 IEEE/ACM International Workshop on Cloud Intelligence (CloudIntelligence) . IEEE, 2021, pp. 19–24
2021
Closest in time.