Introduction
This project implements the connector method proposed in the paper Supporting Confidentiality in Process Mining Using Abstraction and Encryption and Ensuring Confidentiality in Process Mining.
Python package
The implementation has been published as a standard Python package. Use the following command to install the corresponding Python package:
pip install p-connector-dfg
Usage
from p_connector_dfg.privacyPreserving import privacyPreserving
ela_path = ".\intermediate_results\ela_connector.xml"
ela_method = "Connector Method"
ela_desired_analyses = ['directly follows graph', 'process discovery']
activity_activity_matrix_path = r".\intermediate_results\test.csv"
dfg_path = "./DFG.svg"
freq_threshold = 0.0
#Connector structure parameters--------------
relation_depth = True #if you want to have relation depth in the connector structure
trace_length = True # if you want to have trace length in the connector structure
trace_id = True # if you want to have a fake trace id in the connector structure
event_log = "sample_log.xes"
key = 'DEFPASSWORD12345'
pp = privacyPreserving(event_log)
pp.apply_privacyPreserving(key, ela_path, ela_method, ela_desired_analyses, event_log, relation_depth = relation_depth, trace_length = trace_length, trace_id = trace_id)
pp.result_maker_ela(ela_path, True,True, True, freq_threshold, dfg_path, activity_activity_matrix_path = activity_activity_matrix_path,key = key)
1 maintainer
Release files for p-connector-dfg 0.0.14
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| p_connector_dfg-0.0.14.tar.gz | 5.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| p_connector_dfg-0.0.14-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 24.2 kB
Release files / p_connector_dfg-0.0.14.tar.gz
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