Self-Supervised Log Parsing

Taylor, W.L.: Cloze procedure: A new tool for measuring readability. Journalism quarterly 30

1953

Earlier work this paper cites.

Jiang, Z.M., Hassan, A.E., Hamann, G., Flora, P.: An automated approach for abstracting execution logs to execution events. Journal of Software Maintenance and Evolution: Research and Practice 20

2008

Earlier work this paper cites.

Fu, Q., Lou, J.G., Wang, Y., Li, J.: Execution anomaly detection in distributed systems through unstructured log analysis. In: 2009 ninth IEEE international conference on data mining. pp. 149–158. IEEE (2009)

2009

Earlier work this paper cites.

Xu, W., Huang, L., Fox, A., Patterson, D., Jordan, M.I.: Detecting large-scale system problems by mining console logs. In: Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles. pp. 117–132. ACM (2009)

2009

Earlier work this paper cites.

Nagappan, M., Vouk, M.A.: Abstracting log lines to log event types for mining software system logs. In: 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010). pp. 114–117. IEEE (2010)

2010

Earlier work this paper cites.

Tang, L., Li, T., Perng, C.S.: Logsig: Generating system events from raw textual logs. In: Proceedings of the 20th ACM international conference on Information and knowledge management. pp. 785–794 (2011)

2011

Earlier work this paper cites.

Mizutani, M.: Incremental mining of system log format. In: 2013 IEEE International Conference on Services Computing. pp. 595–602. IEEE (2013)

2013

Earlier work this paper cites.

Du, M., Li, F.: Spell: Streaming parsing of system event logs. In: Data Mining (ICDM), 2016 IEEE 16th International Conference on. pp. 859–864. IEEE (2016)

2016

Cited alongside, same era.

Hamooni, H., Debnath, B., Xu, J., Zhang, H., Jiang, G., Mueen, A.: Logmine: Fast pattern recognition for log analytics. In: CIKM (2016)

2016

Cited alongside, same era.

He, P., Zhu, J., He, S., Li, J., Lyu, M.R.: An evaluation study on log parsing and its use in log mining. In: 2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). pp. 654–661. IEEE (2016)

2016

Cited alongside, same era.

Nandi, A., Mandal, A., Atreja, S., Dasgupta, G.B., Bhattacharya, S.: Anomaly detection using program control flow graph mining from execution logs. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 215–224 (2016)

2016

Cited alongside, same era.

Zhu, L., Laptev, N.: Deep and confident prediction for time series at uber. In: Data Mining Workshops (ICDMW), 2017 IEEE International Conference on. pp. 103–110. IEEE (2017)

2017

Later among the works it cites.

Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

Original

2018

Later among the works it cites.

Messaoudi, S., Panichella, A., Bianculli, D., Briand, L., Sasnauskas, R.: A search-based approach for accurate identification of log message formats. In: Proceedings of the 26th Conference on Program Comprehension. pp. 167–177 (2018)

2018

Later among the works it cites.

Nedelkoski, S., Cardoso, J., Kao, O.: Anomaly detection and classification using distributed tracing and deep learning. In: 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). pp. 241–250 (May 2019). https://doi.org/10.1109/CCGRID.2019.00038

2019