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

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