Specification-Driven Multi-Perspective Predictive Business Process Monitoring (Extended Version)

TL;DR

This paper introduces a flexible specification language for diverse business process prediction tasks and an automatic model creation mechanism, enabling tailored predictive analytics across different domains.

Contribution

It proposes a novel language for specifying various prediction tasks and an automated method for generating corresponding models, addressing limitations of prior work.

Findings

Prototype implementation demonstrates feasibility.

Experiments validate effectiveness on real-life logs.

Supports multiple prediction types with a unified approach.

Abstract

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Thus, different from previous studies, our approach enables us to deal with various kinds of prediction tasks based on the given specification. A prototype implementing our approach has been developed and experiments using a real-life event log have been conducted.