  Software applications, especially Enterprise Resource Planning (ERP) systems,
are crucial to the day-to-day operations of many industries. Therefore, it is
essential to maintain these systems effectively using tools that can identify,
diagnose, and mitigate their incidents. One promising data-driven approach is
the Subgroup Discovery (SD) technique, a data mining method that can
automatically mine incident datasets and extract discriminant patterns to
identify the root causes of issues. However, current SD solutions have
limitations in handling complex target concepts with multiple attributes
organized hierarchically. To illustrate this scenario, we examine the case of
Java out-of-memory incidents among several possible applications. We have a
dataset that describes these incidents, including their context and the types
of Java objects occupying memory when it reaches saturation, with these types
arranged hierarchically. This scenario inspires us to propose a novel Subgroup
Discovery approach that can handle complex target concepts with hierarchies. To
achieve this, we design a pattern syntax and a quality measure that ensure the
identified subgroups are relevant, non-redundant, and resilient to noise. To
achieve the desired quality measure, we use the Subjective Interestingness
model that incorporates prior knowledge about the data and promotes patterns
that are both informative and surprising relative to that knowledge. We apply
this framework to investigate out-of-memory errors and demonstrate its
usefulness in incident diagnosis. To validate the effectiveness of our approach
and the quality of the identified patterns, we present an empirical study. The
source code and data used in the evaluation are publicly accessible, ensuring
transparency and reproducibility.
