Time prediction based on process mining

By Wil M.P. van der Aalst, Helen Schonenberg, Minseok Song. Information Systems, 2011

552 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/4bb7bc2b5622695e6bc36c0b5b4a0fcfab37b1cd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Marlon Dumas
2. Marcello La Rosa
3. Chiara Ghidini
4. Niek Tax
5. Chiara Di Francescomarino
6. Fabrizio Maria Maggi
7. Wil M.P. van der Aalst

In Information Systems

1. Marlon Dumas
2. Marcello La Rosa
3. Chiara Ghidini
4. Niek Tax
5. Chiara Di Francescomarino
6. Fabrizio Maria Maggi
7. Wil M.P. van der Aalst

In Management Information Systems

1. Marlon Dumas
2. Marcello La Rosa
3. Chiara Ghidini
4. Chiara Di Francescomarino
5. Fabrizio Maria Maggi
6. Wil M.P. van der Aalst

Citations in the corpus

• Predictive Business Process Monitoring with LSTM Neural Networks (2016, methodology): “Time prediction baselines Set abstraction [1] 6.”
• Predictive Monitoring of Business Processes (2013, background): “.9). 6 Related Work In the literature, there are some works that provide approaches for generating predictions and recommendations during process execution and are focused on the time perspective. In [3,2], the authors present a set of approaches based on annotated transition systems containing time”