Case Studies of Causal Discovery from IT Monitoring Time Series

Autor:	Aït-Bachir, Ali, Assaad, Charles K., de Bignicourt, Christophe, Devijver, Emilie, Ferreira, Simon, Gaussier, Eric, Mohanna, Hosein, Zan, Lei
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Statistics - Applications Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	Information technology (IT) systems are vital for modern businesses, handling data storage, communication, and process automation. Monitoring these systems is crucial for their proper functioning and efficiency, as it allows collecting extensive observational time series data for analysis. The interest in causal discovery is growing in IT monitoring systems as knowing causal relations between different components of the IT system helps in reducing downtime, enhancing system performance and identifying root causes of anomalies and incidents. It also allows proactive prediction of future issues through historical data analysis. Despite its potential benefits, applying causal discovery algorithms on IT monitoring data poses challenges, due to the complexity of the data. For instance, IT monitoring data often contains misaligned time series, sleeping time series, timestamp errors and missing values. This paper presents case studies on applying causal discovery algorithms to different IT monitoring datasets, highlighting benefits and ongoing challenges. Comment: Accepted to the UAI 2023 Workshop on The History and Development of Search Methods for Causal Structure
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2307.15678 Zobrazit plný text záznamu View this record from Arxiv