Process model forecasting and change exploration using time series analysis of event sequence data-Reference-Cited by-同舟云学术

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Process model forecasting and change exploration using time series analysis of event sequence data

Published:2023-05 Issue: Volume:145 Page:102145
ISSN:0169-023X
Container-title:Data & Knowledge Engineering
language:en
Short-container-title:Data & Knowledge Engineering

Author:

De Smedt Johannes,Yeshchenko Anton,Polyvyanyy Artem,De Weerdt Jochen,Mendling Jan

Funder

KU Leuven

KU Leuven Research Council

Einstein Foundation Berlin

Permanent Conference of the Ministers of Education of the German Federal States

Australian Research Council

Fonds Wetenschappelijk Onderzoek

Publisher

Elsevier BV

Subject

Information Systems and Management

Reference42 articles.

1. Process Mining. Data Science in Action;van der Aalst,2016

2. Predictive process monitoring methods: Which one suits me best?;Di Francescomarino,2018

3. Predictive business process monitoring with LSTM neural networks;Tax,2017

4. Prediction of remaining service execution time using stochastic Petri nets with arbitrary firing delays;Rogge-Solti,2013

5. Outcome-oriented predictive process monitoring: Review and benchmark;Teinemaa;ACM Trans. Knowl. Discov. Data,2019

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