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Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns

This repository contains the source code of a new prescriptive process monitoring system that provides process analysts with recommendations for achieving a positive outcome of an ongoing process. The recommendations are temporal relations between the activities being executed in the process. The system description under review, a preprint can be found here.

Repository Structure

  • media/input contains the input logs in .csv format. Before reproducing the experiments it is necessary to download and unzip the log folder from here;
  • media/output contains the numeric results regarding the performance of the prescriptive system;
  • src contains the backbone code;
  • settings.py contains the main settings for the experiments as described in the paper below;
  • dataset_figures.py is a Python script to extract the dataset figures and save them in a .csv file in the media/output folder;
  • run_experiments.py is the main Python script for running the experiments;
  • gather_results.py is a Python script for aggregating the results of each dataset and presenting in a more understandable format.

Requirements

The following Python packages are required:

  • numpy tested with version 1.19.2;
  • PM4PY tested with version 2.2.21;
  • sklearn tested with version 0.24.1;
  • pandas tested with version 1.1.5.

Usage

The system has been tested with Python 3.6.9. After installing the requirements, please download this repository.

Running the Experiments

Type:

 $ python run_experiments.py.py -j num_jobs

where num_jobs is an integer indicating the number of jobs to execute in parallel. Setting num_jobs to -1 will use all the processors.

Gathering the Results

After running the experiments, type:

 $ python gather_results.py

to have an aggregation of the results for each dataset. Such aggregation are found in the files in the media/output folder.

Citing

If you use our prescriptive process in your research, please use the following BibTeX entry

SOON