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Software applications, especially Enterprise Resource Planning (ERP) systems,are crucial to the day-to-day operations of many industries. Therefore, it isessential to maintain these systems effectively using tools that can identify,diagnose, and mitigate their incidents. One promising data-driven approach isthe Subgroup Discovery (SD) technique, a data mining method that canautomatically mine incident datasets and extract discriminant patterns toidentify the root causes of issues. However, current SD solutions havelimitations in handling complex target concepts with multiple attributesorganized hierarchically. To illustrate this scenario, we examine the case ofJava out-of-memory incidents among several possible applications. We have adataset that describes these incidents, including their context and the typesof Java objects occupying memory when it reaches saturation, with these typesarranged hierarchically. This scenario inspires us to propose a novel SubgroupDiscovery approach that can handle complex target concepts with hierarchies. Toachieve this, we design a pattern syntax and a quality measure that ensure theidentified subgroups are relevant, non-redundant, and resilient to noise. Toachieve the desired quality measure, we use the Subjective Interestingnessmodel that incorporates prior knowledge about the data and promotes patternsthat are both informative and surprising relative to that knowledge. We applythis framework to investigate out-of-memory errors and demonstrate itsusefulness in incident diagnosis. To validate the effectiveness of our approachand the quality of the identified patterns, we present an empirical study. Thesource code and data used in the evaluation are publicly accessible, ensuringtransparency and reproducibility.

Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets