Log-based Evaluation of Label Splits for Process Models

TL;DR

This paper introduces a statistical evaluation method for label refinements in process mining, specifically applied to smart home sensor data, to improve process model accuracy and interpretability.

Contribution

It proposes a novel statistical approach to evaluate label refinements, enhancing the extraction of meaningful process models from complex sensor data.

Findings

The method successfully identified beneficial label refinements.

Refinements improved process model precision.

Application demonstrated on smart home data.

Abstract

Process mining techniques aim to extract insights in processes from event logs. One of the challenges in process mining is identifying interesting and meaningful event labels that contribute to a better understanding of the process. Our application area is mining data from smart homes for elderly, where the ultimate goal is to signal deviations from usual behavior and provide timely recommendations in order to extend the period of independent living. Extracting individual process models showing user behavior is an important instrument in achieving this goal. However, the interpretation of sensor data at an appropriate abstraction level is not straightforward. For example, a motion sensor in a bedroom can be triggered by tossing and turning in bed or by getting up. We try to derive the actual activity depending on the context (time, previous events, etc.). In this paper we introduce the notion of label refinements, which links more abstract event descriptions with their more refined counterparts. We present a statistical evaluation method to determine the usefulness of a label refinement for a given event log from a process perspective. Based on data from smart homes, we show how our statistical evaluation method for label refinements can be used in practice. Our method was able to select two label refinements out of a set of candidate label refinements that both had a positive effect on model precision.