pystackt 0.0.2

Data preparation for object-centric process mining.

PyStack't (pystackt)

PyStack't (pystackt) is a Python package based on Stack't that supports data preparation for object-centric process mining.

While Stack't is a proof-of-concept on how to embed the data transformations needed for object-centric process mining into an (existing) data stack, it's not very user-friendly for ad-hoc use. PyStack't aims to fill that gap by exposing some functionality in a Python package.

📦 Installation

You can install pystackt using pip:

pip install pystackt

⛏️🐙 Extracting object-centric event logs from Github

📝 Example

from pystackt import *

get_github_log(
    GITHUB_ACCESS_TOKEN="insert_your_github_access_token_here",
    repo_owner="LienBosmans",
    repo_name="stack-t",
    max_issues=None, # None returns all issues, can also be set to an integer to extract a limited data set
    quack_db="./stackt.duckdb"
)

🔑 Generating a GitHub Access Token

To generate a GitHub access token, go to GitHub Developer Settings, click "Generate new token (classic)", and proceed without selecting any scopes (leave all checkboxes unchecked). Copy the token and store it securely, as it won’t be shown again.

🔍 Viewing Data

This function creates a DuckDB database file. To explore the data, you'll need a database manager. You can follow this DuckDB guide to download and install DBeaver for easy access.

📜 Data Usage Policies

Please ensure that you use the extracted data in compliance with GitHub policies, including Information Usage Restrictions and API Terms.

📤 Export to OCEL 2.0

📝 Example

from pystackt import *

export_to_ocel2(
    quack_db="./stackt.duckdb",
    schema_in="main",
    schema_out="ocel2",
    sqlite_db="./ocel2_stackt.sqlite"
)

ℹ️ More information

• The OCEL 2.0 standard is defined in OCEL (Object-Centric Event Log) 2.0 Specification.
• To explore event logs in the OCEL 2.0 format, you can use Ocelot.