An Interpretable method for process remaining time prediction based on distance metric learning

Hosseini Nezhad, Zahra; Aliakbary, Sadegh; Ghavamizadeh, Ramak; Malek, Hamed

doi:10.48308/jicse.2024.234242.1029

An Interpretable method for process remaining time prediction based on distance metric learning

Document Type : Original Article

Authors

Zahra Hosseini Nezhad ¹

Sadegh Aliakbary ²

Ramak Ghavamizadeh ³

Hamed Malek ⁴

¹ Department of CSE, Shahid Beheshti University

² Software and information systems

³ گروه نرم افزار و سامانه های اطلاعاتی

⁴ گروه هوش مصنوعی، رباتیک و رایانش شناختی

https://doi.org/10.48308/jicse.2024.234242.1029

Abstract

Process mining is a new research area that bridges the gap between data science and process science. Among the subfields of process mining, predictive process monitoring aims to predict process features such as the next event, outcome, and remaining time. In recent years, increasing research has been conducted in the area of remaining time prediction.
In this paper, we present an interpretable method for remaining time prediction. Interpretability is an advantageous feature for a prediction method, because it provides the necessary advice and warnings to the organization’s experts during the process execution when it is used in a recommender system. In the proposed method, we utilize distance metric learning methods to develop a distance function for process events. The distance function can be used to find the most similar cases to the intended case, and then the remaining time of the similar cases is used as the indicator of the remaining time of the intended case. Our proposed method has been evaluated on three datasets, and the evaluations revealed that it significantly outperforms the state-of-the-art baseline in two datasets while achieving comparable accuracy in the third dataset. Additionally, our proposed method provides interpretation, while the competitor methods are not interpretable.

Keywords

Business Process

Process Mining

Remaining Time Prediction

Distance Metric

Interpretable Prediction

Subjects