Discovering Process-Based Drivers for Case-Level Outcome Explanation

Peng Li, Hantian Zhang, Xu Chu, Alexander Seeliger, Cong Yu

Abstract: Process mining has shown great impact in improving business Key Performance Indicators (KPIs), which are typically measured as aggregations over case-level outcomes. A commonly encountered key question in achieving such impact is understanding the underlying reasons for why a certain outcome appears in some cases (e.g., why certain cases take long to finish). We use the term drivers to refer to explanations for case-level outcomes. We hypothesize that how process is run, in other words, process traces, directly influences case-level outcomes, and hence KPIs. In this paper, we propose a new method to automatically and efficiently discover process-based drivers that are effective, significant and interpretable. We formally define the problem of driver discovery as a constrained optimization problem. Given that the problem is NP-hard, we develop efficient greedy algorithms to solve the problem. We evaluate our method on real-world datasets to demonstrate the effectiveness and efficiency of our approach.