Attention Based Selection of Log Templates for Automatic Log Analysis
| Vortragende(r) | Vincenzo Pace | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Pawel Bielski | |
| Termin | Fr 2. Dezember 2022 | |
| Vortragsmodus | in Präsenz | |
| Kurzfassung | Log parsing is an essential preprocessing step in text log data analysis, such as anomaly detection in cloud system monitoring.
However, selecting the optimal log parsing algorithm for a concrete task is difficult. Many algorithms exist to choose from, and each needs proper parametrization. The selected algorithm applies to the whole dataset and with parameters determined independently of the data analysis task, which is not optimal. In this work, we evaluate a new attention-based method to automatically select the optimal log parsing algorithm for the data analysis task. The method was initially proposed in the Master Thesis and showed promising results on one log parsing algorithm and one dataset. In this work, we plan to test whether the algorithm is helpful for other algorithms and datasets. | |