A Method for the Joint Analysis of Numerical and Textual IT-System Data to Predict Critical System States

Autor:	Stefan Rass, Patrick Kubiak, Stephan Schneider, Martin Pinzger
Rok vydání:	2021
Předmět:	Set (abstract data type) Variable (computer science) Artificial neural network Computer science Process (computing) Feature (machine learning) Data mining USable computer.software_genre computer Lead time Random forest
Zdroj:	Communications in Computer and Information Science ISBN: 9783030830069 ICSOFT (Selected Papers)
DOI:	10.1007/978-3-030-83007-6_12
Popis:	We present a method for the joint analysis of textual and numerical IT-system data usable to predict possibly critical system states. Towards a comparative discussion culminating in a justified model and method choice, we apply logistic regression, random forest and neural networks to the prediction of critical system states. Our models consume a set of different monitoring performance metrics and log file events. To ease the analysis of IT-systems, our models judge the future system state using one binary outcome variable for the system state’s criticality as “alarm” or “no alarm”. Moreover, we use feature importance measures to give IT-operators guidance on which system parameters, i.e., features, to consider primarily when responding to an alarm. We evaluate our models using different configurations, including (among others) the demanded lead time window for incident response, and a set of common performance measures. This paper is an extension to previous work that adds details on how to jointly process textual and numerical data.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::04da146e25825c8749bd845d8dd95130 https://doi.org/10.1007/978-3-030-83007-6_12 Zobrazit plný text záznamu