A Recovery Module for an Incident Management Assistant with Missing and Imbalanced Data - Bonato, Alisson N.; Marques, Jade S. Hatanaka; Kumar, Rajnish (Anais da Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região Sul (ERAMIA-RS); 2025: Anais da I Escola Regional de Aprendizado de Máquina e Inteligência Artificial da Região SulDO - 10.5753/eramiars.2025.16747, 2025)
In Information Technology (IT) Service Management, modern approaches leveraging Artificial Intelligence for IT Operations (AIOps) typically rely on abundant, high-quality observability data. However, many real-world environments operate under severe data constraints, such as scarce historical records and incomplete information. This paper introduces AIMA+, an incident management assistant designed specifically for these challenging conditions. Given an incident’s textual description as input, AIMA+ outputs a ranked list of predicted recovery actions, retrieves similar past incidents, and generates a synthesized textual summary to guide operators. Our core contribution is a methodology that addresses data incompleteness through a Large Language Model (LLM)-based data augmentation strategy and data scarcity with an interpretable, multi-pronged framework. Our experiments show that augmenting the dataset with expert-guided LLM classifications dramatically improved predictive performance, increasing the macro F1-score from a baseline of 0.2 to 0.6. This work presents a pragmatic blueprint for developing effective AIOps decision support tools in realistic, imperfect industrial settings.