Predictive Monitoring of Business Processes

By Fabrizio Maria Maggi, Chiara Di Francescomarino, Marlon Dumas, Chiara Ghidini. International Conference on Advanced Information Systems Engineering, 2013

312 citations recorded by the source corpus.

Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business constraints during business process execution. At any point during an execution of a process, the user can define business constraints in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business constraint. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business constraints. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital.

Subfields: Artificial Intelligence, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/c2a9a4073251800422b0d874262da5e6e469025b

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. Niek Tax
4. Wil M.P. van der Aalst

In Information Systems

1. Marlon Dumas
2. Marcello La Rosa
3. Niek Tax
4. Wil M.P. van der Aalst

In Management Information Systems

1. Marlon Dumas
2. Marcello La Rosa
3. Wil M.P. van der Aalst

Citations in the corpus

• Predictive Business Process Monitoring with LSTM Neural Networks (2016, background): “A range of such techniques have been proposed for a variety of prediction tasks: predicting the next activity [2], predicting the future path (continuation) of a running case [25], predicting the remaining cycle time [27], predicting deadline violations [22] and predicting the fulfillment of a prope”