Table of Contents

HMM Experimenter

The HMM Experimenter reads in a number of Petri net process models from a given input folder, converts these models into HMMs, generates logs for different levels of noise, and evaluates the fitness of these noisy logs for a number of fitness metrics.


Before you start the experiment, you need to put all models that should be evaluated in a folder called InputModels. This folder must be located in the ProM base directory. For example, if you are running ProM from Eclipse, this folder would be located in your ProM project folder (where also the .ini files are).

The models in this input folder should fulfill the following requirements:

To start the HMM Experimenter, you need to make sure to have some window with an attached object selected in ProM. In principle, the HMM experimenter does not need any input, but this is a technical limitation in ProM—so, just open some log, or one of your Petri net models in the InputModels folder, and then you can start the plug-in.

HMM Experimenter Settings

The HMM Experimenter offers a number of parameters to configure the experiment. The following parameters are available:

HMM Experimenter Settings Figure 1. HMM Experimenter settings allow for the configuration of the experiment

HMM Experimenter Results

Based on your configuration, the HMM Experimenter will then create logs containing different levels of observation noise and transition noise (refer to [2] for details on the noise generation), for each of the models contained in your InputModels folder.

The produced output will be located in the following new folders, also located in the ProM base directory:

To generate the evaluation graphs, you can open either the FitnessEvaluation_lines.gpl or the FitnessEvaluation_points.gpl file in the corresponding evaluation folder, which will automatically create a PS file of the graph in that same folder.


[1] A. Rozinat, M. Veloso, and W.M.P. van der Aalst. Evaluating the Quality of Discovered Process Models. In W. Bridewell, T. Calders, A.K. de Medeiros, S. Kramer, M. Pechenizkiy, and L. Todorovski, editors, Proceedings of IPM 2008 induction of process models, Second International Workshop on the Induction of Process Models, at ECML PKDD 2008, 15 September 2008, Antwerp, Belgium, pages 45–52, 2008.

[2] A. Rozinat, M. Veloso, and W.M.P. van der Aalst. Using Hidden Markov Models to Evaluate the Quality of Discovered Process Models. Extended Version. BPM Center Report BPM-08-10,, 2008.