  The management of modern IT systems poses unique challenges, necessitating
scalability, reliability, and efficiency in handling extensive data streams.
Traditional methods, reliant on manual tasks and rule-based approaches, prove
inefficient for the substantial data volumes and alerts generated by IT
systems. Artificial Intelligence for Operating Systems (AIOps) has emerged as a
solution, leveraging advanced analytics like machine learning and big data to
enhance incident management. AIOps detects and predicts incidents, identifies
root causes, and automates healing actions, improving quality and reducing
operational costs. However, despite its potential, the AIOps domain is still in
its early stages, decentralized across multiple sectors, and lacking
standardized conventions. Research and industrial contributions are distributed
without consistent frameworks for data management, target problems,
implementation details, requirements, and capabilities. This study proposes an
AIOps terminology and taxonomy, establishing a structured incident management
procedure and providing guidelines for constructing an AIOps framework. The
research also categorizes contributions based on criteria such as incident
management tasks, application areas, data sources, and technical approaches.
The goal is to provide a comprehensive review of technical and research aspects
in AIOps for incident management, aiming to structure knowledge, identify gaps,
and establish a foundation for future developments in the field.
