This tool is able to extract a stochastic process model via DDS techniques. This code can perform the next tasks:
- Extract a stochastic process model using an event log as input.
- Generate sequences of activities and roles using the stochastic process model.
- Assess the similarity between the original sequences and the generated ones.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Tested with Python 3.8. Install the necessary packages with
pip install -r requirements.txtTo install the CLI-tool and the library version from the root directory run:
$ pip install -e .Invoke the CLI tool with either of these:
Once created the environment, you can execute the tool from a terminal specifying the input event log name and any of the following parameters:
Discovery:
--file (required): event log in XES format.--evaluate/--no-evaluate (optional, default=True): Refers to whether or not you want to perform a final assessment of the accuracy of the final simulation model.--mining_alg (optional, default='sm1'): version of SplitMiner to use. Available options: 'sm1', 'sm2', 'sm3'.--s_gen_max_eval (optional, default='30'): Number of trials used by the optimizer in the discovery face.--exp_reps (optional, default='5'): number of repetition per trial.
Example of execution:
$ spmd discover --file ..\data\Production.xes --exp_reps 10Generation:
--generative_model (required)- Stochastic process model (BPMN model enhanced with parameters)--evaluate/--no-evaluate (optional, default=True): Refers to whether or not you want to perform a final assessment of the accuracy of the final simulation model.--num_inst- Number of case instances desired on each execution.--exp_reps (optional, default='5'): number of repetition per execution.
Example of execution:
$ spmd generate --generative_model ..\data\Production.bpmn --exp_reps 10