The genuine supervision of modern IT systems brings new opportunities and challenges by making available big data streams that, if properly analysed, can support high standards of scalability, reliability and efficiency. Rule-based inference engines on streaming data are a key component of maintenance systems for detecting anomalies and automatizing their resolution, but they remain confined to simple and general rules, a lesson learned from the expert systems era. Artificial Intelligence for Operations Systems (AIOps) propose to take advantage of advanced analytics, such as machine learning and data mining on big data, to improve every step of supervision systems, such as incident management (detection, triage, root cause analysis, automated healing). However, the best AIOPs techniques often rely on ``opaque'' models, strongly limiting their adoption. In this thesis, we aim at studying how Subgroup Discovery can help AIOps. This data mining offers possibilities to extract hypotheses from data, resp. from predictive models, helping the experts to understand the underlying processes generating the data, resp. predictions. To ensure relevancy of our propositions, this project involves both data mining researchers and practitioners from Infologic, a French software editor.