Determining Window Sizes using Species Estimation for Accurate Process Mining over Streams

TL;DR

This paper introduces a dynamic window sizing method for streaming process mining, leveraging species estimation techniques to improve analysis accuracy and robustness amid concept drifts in real-time event streams.

Contribution

It proposes a novel approach that dynamically adjusts window sizes using species estimation estimators, addressing limitations of static windows in process mining.

Findings

Improved accuracy over static window approaches

Enhanced robustness to concept drifts

Effective on real-world datasets

Abstract

Streaming process mining deals with the real-time analysis of event streams. A common approach for it is to adopt windowing mechanisms that select event data from a stream for subsequent analysis. However, the size of these windows denotes a crucial parameter, as it influences the representativeness of the window content and, by extension, of the analysis results. Given that process dynamics are subject to changes and potential concept drift, a static, fixed window size leads to inaccurate representations that introduce bias in the analysis. In this work, we present a novel approach for streaming process mining that addresses these limitations by adjusting window sizes. Specifically, we dynamically determine suitable window sizes based on estimators for the representativeness of samples as developed for species estimation in biodiversity research. Evaluation results on real-world data sets show improvements over existing approaches that adopt static window sizes in terms of accuracy and robustness to concept drifts.