3.6 KiB
3.6 KiB
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split-miner
Description
split-miner is a Python implementation of the Split Miner algorithm for automated discovery of BPMN process models from event logs.
Split Miner (Augusto et al., 2017/2019) produces simple process models with low branching complexity and consistently high and balanced fitness and precision, while being guaranteed to produce deadlock-free models with concurrency.
This package implements Split Miner 1.0 with the following pipeline:
- DFG and loops discovery
- Concurrency discovery
- Edge filtering
- Split gateways discovery
- Join gateways discovery (via RPST / SPQR-tree)
- OR-joins minimization (via dominator tree)
Features:
- Pure Python --- no compiled extensions.
- Implements Split Miner 1.0 from the original papers.
- Uses SPQR-tree for correct RPST computation.
- Typed package with PEP 561 support.
- Requires Python 3.10 or later.
Installation
You can install split-miner with pip:
pip install split-miner
You may also install the latest source from the split-miner GitHub repository.
pip install git+https://github.com/imacat/split-miner.git
Quick Start
from split_miner import BPMNModel, split_miner # Create an event log (trace -> frequency) traces: dict[tuple[str, ...], int] = { ("a", "b", "c", "d"): 10, ("a", "c", "b", "d"): 10, } # Discover a BPMN model model: BPMNModel = split_miner(traces) # Inspect the model print(f"Tasks: {len(model.tasks)}") print(f"Gateways: {len(model.gateways)}") print(f"Edges: {len(model.edges)}")
Parameters
- epsilon (float, 0--1): Controls concurrency detection sensitivity. Lower values require more balanced directly-follows frequencies to detect concurrency. Default: 0.33.
- eta (float, 0--1): Controls edge filtering / retention. Lower values retain more edges, resulting in higher fitness at the cost of lower precision. Default: 0.8.
References
- A. Augusto, R. Conforti, M. Dumas, M. La Rosa, and A. Polyvyanyy, "Split Miner: Automated Discovery of Accurate and Simple Business Process Models from Event Logs," Knowledge and Information Systems, vol. 59, no. 2, pp. 251--284, 2019. doi:10.1007/s10115-018-1214-x
- A. Augusto, R. Conforti, M. Dumas, M. La Rosa, and A. Polyvyanyy, "Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs," Proc. ICDM 2017, pp. 1--10, 2017. doi:10.1109/ICDM.2017.9
Acknowledgments
This project was implemented from scratch in Python based on the original Split Miner papers.
Development was assisted by Claude Code (Anthropic).
Copyright
Copyright (c) 2026 imacat.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.