Log-Skeleton-Backend

Status

📄 Documentation

The project provides a documentation in the GitHub Wiki page of this project.

It covers different topics like:

• Implementation of the Log-Skeleton algorithm
• XES Importer
• API setup & usage

👷‍♀️ Installation & Setup

This project requires python version < 3.9.x (some requirements are not compatible with 3.9.x).

To install the required dependencies use the following commands:

🐍 Download and install python 3 (lower than python 3.9).

🚨 Install flake8 as for linting:

pip install flake8 flake8-docstrings

✅ Install pytest for unit testing.

pip install pytest

🌐 Install flask for the REST-API server.

pip install flask

📈 Install pm4py for some process discovery helpers.

pip install pm4py

🚀 Starting the API server

To start the application run:

python -m src.api.server

This command will start a HTTP server for the API.

🌐 Using the API

🎯 Endpoints:

/log-skeleton

This HTTP endpoint will accept an XES event log as the input and return a log-skeleton model based on that model.

Provide an XES event-log in the body of the request for the server.

🔧 Parameters:

• noise-threshold: Number between 0 and 1 to specitfy a noise_threshold for the algorithm.
• extended-trace: Boolean value indicating whether the trace extension will be included or not.

📦 The API-Response:

In case the API gets used as it is inteded to be, it will return a JSON object containing the following items:

• always-after: Contains a list of tuples representing the always-after relationship.
• always-before: Contains a list of tuples representing the always-before relationship.
• equivalence: Contains a list of tuples representing the equivalence relationship.
• never_together: Contains a list of tuples representing the never_together relationship.
• next_one_way: Contains a list of tuples representing the next_one_way relationship.
• next_both_ways: Contains a list of tuples representing the next_both_ways relationship.
• counter: Contains a JSON object representing the counter relationships.
• parameters: Contains a JSON object indicating the parameters applied and further information like IDs of the trace start and trace end.

⛔️ Error codes

In case of an error the API will respond with the appropriate HTTP error code. Further an error description will be provided in the response in the error_msg field.

Examples

The following example will return a log skeleton model for the given log in the body with a noise threshold of 3%. https://<domain>/log-skeleton?noise-threshold=0.03

The following example will return a log skeleton model for the given log in the body with a noise threshold of 10% and it will include the extended traces. https://<domain>/log-skeleton?noise-threshold=0.1&extended-trace=true

The following example will return a 404 error since there is no route called /log-skleeton. https://<domain>/log-skleeton?noise-threshold=0.1