How Can Subgroup Discovery Help AIOps?

TL;DR

This paper explores how Subgroup Discovery, a data mining technique, can enhance AIOps by improving anomaly detection and understanding predictive models in large-scale IT systems.

Contribution

It introduces the application of Subgroup Discovery to AIOps, aiming to improve interpretability and effectiveness of anomaly detection in big data environments.

Findings

Subgroup Discovery can identify meaningful patterns in AIOps data.

It helps explain opaque machine learning models used in AIOps.

The approach enhances the interpretability of anomaly detection results.

Abstract

The genuine supervision of modern IT systems brings new challenges as it requires higher standards of scalability, reliability and efficiency when analysing and monitoring big data streams. Rule-based inference engines are a key component of maintenance systems in detecting anomalies and automating their resolution. However, they remain confined to simple and general rules and cannot handle the huge amount of data, nor the large number of alerts raised by IT systems, a lesson learned from expert systems era. Artificial Intelligence for Operation Systems (AIOps) proposes to take advantage of advanced analytics and machine learning on big data to improve and automate every step of supervision systems and aid incident management in detecting outages, identifying root causes and applying appropriate healing actions. Nevertheless, the best AIOps techniques rely on opaque models, strongly limiting their adoption. As a part of this PhD thesis, we study how Subgroup Discovery can help AIOps. This promising data mining technique offers possibilities to extract interesting hypothesis from data and understand the underlying process behind predictive models. To ensure relevancy of our propositions, this project involves both data mining researchers and practitioners from Infologic, a French software editor.