Detecting and Explaining Drifts in Yearly Grant Applications

TL;DR

This paper introduces an automated method for detecting and explaining concept drift in multivariate business process logs, demonstrated on EU grant application data, enabling detailed root-cause analysis without manual feature selection.

Contribution

The proposed approach automatically detects concept drift in multivariate logs and provides root-cause explanations, eliminating the need for manual feature selection.

Findings

Successfully detected concept drift in real-world data

Enabled detailed root-cause analysis

Did not require manual feature selection

Abstract

During the lifetime of a Business Process changes can be made to the workflow, the required resources, required documents, . . . . Different traces from the same Business Process within a single log file can thus differ substantially due to these changes. We propose a method that is able to detect concept drift in multivariate log files with a dozen attributes. We test our approach on the BPI Challenge 2018 data con- sisting of applications for EU direct payment from farmers in Germany where we use it to detect Concept Drift. In contrast to other methods our algorithm does not require the manual selection of the features used to detect drift. Our method first creates a model that captures the re- lations between attributes and between events of different time steps. This model is then used to score every event and trace. These scores can be used to detect outlying cases and concept drift. Thanks to the decomposability of the score we are able to perform detailed root-cause analysis.