Proceedings-Artikel

XES Tensorflow – Process Prediction using the Tensorflow Deep-Learning Framework

Joerg Evermann; Jana-Rebecca Rehse; Peter Fettke
In: X. Franch; J. RalytĂ©; R. Matulevičius; C. Salinesi; R. Wieringa (Hrsg.). CAiSE 2017 Forum and Doctoral Consortium Papers. Forum at the CAiSE Conference (CAiSE-2017), June 12-16, Essen, Germany, CEUR Workshop Proceedings, 2017.

Abstract

Predicting the next activity of a running process is an important aspect of process management. Recently, artificial neural networks, so called deep-learning approaches, have been proposed to address this challenge. This demo paper describes a software application that applies the Tensorflow deep-learning framework to process prediction. The software application reads industry-standard XES files for training and presents the user with an easy-to-use graphical user interface for both training and prediction. The system provides several improvements over earlier work. This demo paper focuses on the software implementation and describes the architecture and user interface.

Weitere Links

BibTeX

https://arxiv.org/abs/1705.01507