pm4py.algo.discovery.ocel.ocdfg package#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions Contact: info@processintelligence.solutions
Subpackages#
Submodules#
pm4py.algo.discovery.ocel.ocdfg.algorithm module#
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions Contact: info@processintelligence.solutions
- class pm4py.algo.discovery.ocel.ocdfg.algorithm.Variants(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- CLASSIC = <module 'pm4py.algo.discovery.ocel.ocdfg.variants.classic' from 'C:\\Users\\berti\\pm4py-core\\pm4py\\algo\\discovery\\ocel\\ocdfg\\variants\\classic.py'>#
- pm4py.algo.discovery.ocel.ocdfg.algorithm.apply(ocel: OCEL, variant=Variants.CLASSIC, parameters: Dict[Any, Any] | None = None) Dict[str, Any][source]#
Discovers an OC-DFG model from an object-centric event log Reference paper: Berti, Alessandro, and Wil van der Aalst. “Extracting multiple viewpoint models from relational databases.” Data-Driven Process Discovery and Analysis. Springer, Cham, 2018. 24-51.
Parameters#
- ocel
Object-centric event log
- variant
Variant of the algorithm to use: - Variants.CLASSIC
- parameters
Variant-specific parameters
Returns#
- ocdfg
Object-centric directly-follows graph