  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.
