In recent years, Predictive Process Mining (PPM) techniques based on artificial neural networks have evolved as a method for monitoring the future behavior of unfolding business processes and predicting Key Performance Indicators (KPIs). However, many PPM approaches often lack reproducibility, transparency in decision making, usability for incorporating novel datasets and benchmarking, making comparisons among different implementations very difficult. In this paper, we propose SPICE, a Python framework that reimplements three popular, existing baseline deep-learning-based methods for PPM in PyTorch, while designing a common base framework with rigorous configurability to enable reproducible and robust comparison of past and future modelling approaches. We compare SPICE to original reported metrics and with fair metrics on 11 datasets.

翻译：近年来，基于人工神经网络的预测性过程挖掘技术已发展成为监控展开中业务流程未来行为及预测关键绩效指标的方法。然而，许多PPM方法往往缺乏可复现性、决策透明度、整合新数据集与基准测试的可用性，导致不同实现间的比较极为困难。本文提出SPICE，一个基于PyTorch的Python框架，它重新实现了三种现有流行的基于深度学习的PPM基线方法，同时设计了一个具有严格可配置性的通用基础框架，以实现对过去及未来建模方法进行可复现且稳健的比较。我们在11个数据集上将SPICE与原始报告指标及公平指标进行了对比。