PyStack't (pystackt) is a Python package based on Stack't that supports data preparation for object-centric process mining.
PyStack't is published on PyPi and can be installed using pip.
pip install pystackt- Extensive documentation is available via GitHub pages.
- A demo video on Youtube can walk you throught the different functionalities.
- Our BPM 2025 demo paper PyStack't: Real-Life Data for Object-Centric Process Mining is available on CEUR.
⛏️🐙 Extract object-centric event log from GitHub repo (get_github_log)
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",
schema="main"
)📈 Interactive data exploration (start_visualization_app)
from pystackt import *
prepare_graph_data( # only needed once
quack_db="./stackt.duckdb",
schema_in="main",
schema_out="graph_data_prep"
)
start_visualization_app(
quack_db="./stackt.duckdb",
schema="graph_data_prep"
)📤 Export to OCEL 2.0 (export_to_ocel2)
from pystackt import *
export_to_ocel2(
quack_db="./stackt.duckdb",
schema_in="main",
schema_out="ocel2",
sqlite_db="./ocel2_stackt.sqlite"
)We welcome any improvements, big and small, as well as new functionality that supports data preparation for object-centric process mining! For more information, please read the Contributing Guide.