Attention Based Selection of Log Templates for Automatic Log Analysis: Unterschied zwischen den Versionen

Aktuelle Version vom 18. Juli 2023, 22:16 Uhr

Vortragende(r)	Vincenzo Pace
Vortragstyp	Bachelorarbeit
Betreuer(in)	Pawel Bielski
Termin	Fr 21. Juli 2023
Vortragsmodus	in Präsenz
Kurzfassung	Log analysis serves as a crucial preprocessing step in text log data analysis, including anomaly detection in cloud system monitoring. However, selecting an optimal log parsing algorithm tailored to a specific task remains problematic. With many algorithms to choose from, each requiring proper parameterization, making an informed decision becomes difficult. Moreover, the selected algorithm is typically applied uniformly across the entire dataset, regardless of the specific data analysis task, often leading to suboptimal results. In this thesis, we evaluate a novel attention-based method for automating the selection of log parsing algorithms, aiming to improve data analysis outcomes. We build on the success of a recent Master Thesis, which introduced this attention-based method and demonstrated its promising results for a specific log parsing algorithm and dataset. The primary objective of our work is to evaluate the effectiveness of this approach across different algorithms and datasets.

@@ Zeile 2: / Zeile 2: @@
 |vortragender=Vincenzo Pace
 |email=vincenzo.pace@mailbox.org
-|vortragstyp=Proposal
+|vortragstyp=Bachelorarbeit
 |betreuer=Pawel Bielski
-|termin=Institutsseminar/2022-12-02 Zusatztermin
+|termin=Institutsseminar/2023-07-21
 |vortragsmodus=in Präsenz
-|kurzfassung=Log parsing is an essential preprocessing step in text log data analysis, such as anomaly detection in cloud system monitoring.
+|kurzfassung=Log analysis serves as a crucial preprocessing step in text log data analysis, including anomaly detection in cloud system monitoring. However, selecting an optimal log parsing algorithm tailored to a specific task remains problematic.
-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.
+With many algorithms to choose from, each requiring proper parameterization, making an informed decision becomes difficult. Moreover, the selected algorithm is typically applied uniformly across the entire dataset, regardless of the specific data analysis task, often leading to suboptimal results.
-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.
+In this thesis, we evaluate a novel attention-based method for automating the selection of log parsing algorithms, aiming to improve data analysis outcomes. We build on the success of a recent Master Thesis, which introduced this attention-based method and demonstrated its promising results for a specific log parsing algorithm and dataset. The primary objective of our work is to evaluate the effectiveness of this approach across different algorithms and datasets.
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