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Merge branch '5-automation-test-gedi-automatically' into bpm24
Browse files* 5-automation-test-gedi-automatically: (56 commits)
Adds plotter test data
Adds provisory evaluation plotter
Renames test data dir-
Updates gitignore
Moves to data to test dir
Setup generation test with file
Adds multiple experiments to gen
Specifies Python version
Fixes integration ConfigSpace installation for ubuntu
Corrects generation output path
Removes unnecessary conda
Updates github action versions
Adds conda install for ConfigSpace
Removes .checkpoints
Gitignore
Updates github action versions
Fixes test typo
Adds integration test
specifies numpy
Fixes yml
...
This view is limited to 50 files because it contains too many changes.
See raw diff
- .github/workflows/test_gedi.yml +162 -0
- .gitignore +5 -1
- README.md +2 -3
- config.py +3 -3
- config_files/algorithm/benchmark.json +1 -2
- config_files/algorithm/evaluation_plotter.json +7 -5
- config_files/algorithm/experiment_test.json +7 -7
- config_files/algorithm/feature_extraction.json +1 -1
- config_files/algorithm/generation.json +2 -5
- data/2_grid_test.csv +3 -3
- data/{test_2 → test}/gen_el_168.xes +0 -0
- data/{test_2 → test}/gen_el_169.xes +0 -0
- data/test/grid_feat.csv +3 -0
- data/test/plotter/1_enve_feat.csv +12 -0
- data/test/plotter/grid_1objectives_enve.csv +12 -0
- execute_grid_experiments.py +1 -1
- gedi/__init__.py +8 -0
- {tag → gedi}/analyser.py +3 -3
- {tag → gedi}/augmentation.py +1 -1
- {tag → gedi}/benchmark.py +3 -3
- {tag → gedi}/features.py +1 -1
- {tag → gedi}/generator.py +2 -2
- {tag → gedi}/plotter.py +6 -5
- {tag → gedi}/utils/algorithms/__init__.py +0 -0
- {tag → gedi}/utils/algorithms/tsne.py +0 -0
- {tag → gedi}/utils/array_tools.py +0 -0
- {tag → gedi}/utils/io_helpers.py +0 -0
- {tag → gedi}/utils/matrix_tools.py +0 -0
- main.py +9 -9
- notebooks/.ipynb_checkpoints/augmentation-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/benchmarking_process_discovery-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/bpic_generability_pdm-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/data_exploration-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/experiment_generator-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/feature_distributions-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/feature_exploration-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/feature_performance_similarity-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/feature_selection-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/feature_variance-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/gedi_representativeness-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/grid_objectives-checkpoint.ipynb +0 -376
- notebooks/.ipynb_checkpoints/oversampling-checkpoint.ipynb +0 -6
- notebooks/.ipynb_checkpoints/performance_feature_correlation-checkpoint.ipynb +0 -6
- notebooks/.ipynb_checkpoints/pt_gen-checkpoint.ipynb +0 -0
- notebooks/.ipynb_checkpoints/statistics_tasks_to_datasets-checkpoint.ipynb +0 -818
- notebooks/.ipynb_checkpoints/test_feed-checkpoint.ipynb +0 -0
- notebooks/benchmarking_process_discovery.ipynb +2 -2
- notebooks/bpic_generability_pdm.ipynb +1 -1
- notebooks/experiment_generator.ipynb +2 -2
- notebooks/feature_distributions.ipynb +1 -1
.github/workflows/test_gedi.yml
ADDED
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name: GEDI Test
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# Specifies when the action should run
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on:
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pull_request:
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branches:
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- main
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# Specifies the jobs that are to be run
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jobs:
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test_feature-extraction:
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runs-on: ubuntu-latest
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# Setting up a python envronment for the test script to run
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: 3.9
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- name: Install feeed
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run: |
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python -m pip install --upgrade pip
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pip install .
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- name: Run test
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run:
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python main.py -o config_files/options/baseline.json -a config_files/algorithm/feature_extraction.json
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- name: Compare output
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run: diff data/test_feat.csv data/test_feat.csv
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test_generation:
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runs-on: ubuntu-latest
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# Setting up a python envronment for the test script to run
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steps:
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+
- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: 3.9
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- name: Install dependencies
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run: |
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sudo apt-get install build-essential python3 python3-dev
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- name: Install feeed
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run: |
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python -m pip install --upgrade pip
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pip install .
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- name: Run test
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run:
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python main.py -o config_files/options/baseline.json -a config_files/algorithm/generation.json
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- name: Compare output
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run: diff output/features/grid_feat/2_enself_rt20v/genELexperiment2_07_04.json output/features/grid_feat/2_enself_rt20v/genELexperiment2_07_04.json
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test_benchmark:
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runs-on: ubuntu-latest
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# Setting up a python envronment for the test script to run
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steps:
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- name: Checkout code
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| 71 |
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uses: actions/checkout@v4
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+
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+
- name: Set up Python
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| 74 |
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uses: actions/setup-python@v5
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with:
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python-version: 3.9
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- name: Install feeed
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run: |
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python -m pip install --upgrade pip
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pip install .
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- name: Run test
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run:
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python main.py -o config_files/options/baseline.json -a config_files/algorithm/benchmark.json
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| 86 |
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- name: Compare output
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| 88 |
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run: diff output/benchmark/test_benchmark.csv output/benchmark/test_benchmark.csv
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test_augmentation:
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runs-on: ubuntu-latest
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| 92 |
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# Setting up a python envronment for the test script to run
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| 94 |
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steps:
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| 95 |
+
- name: Checkout code
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| 96 |
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uses: actions/checkout@v4
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| 97 |
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- name: Set up Python
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| 99 |
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uses: actions/setup-python@v5
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| 100 |
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with:
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| 101 |
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python-version: 3.9
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- name: Install feeed
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run: |
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python -m pip install --upgrade pip
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pip install .
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- name: Run test
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run:
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python main.py -o config_files/options/baseline.json -a config_files/algorithm/augmentation.json
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test_evaluation-plotter:
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| 113 |
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runs-on: ubuntu-latest
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| 114 |
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| 115 |
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# Setting up a python envronment for the test script to run
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| 116 |
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steps:
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| 117 |
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- name: Checkout code
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| 118 |
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uses: actions/checkout@v4
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| 119 |
+
|
| 120 |
+
- name: Set up Python
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| 121 |
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uses: actions/setup-python@v5
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| 122 |
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with:
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python-version: 3.9
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| 124 |
+
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| 125 |
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- name: Install dependencies
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| 126 |
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run: |
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| 127 |
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sudo apt-get install build-essential python3 python3-dev
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| 128 |
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| 129 |
+
- name: Install feeed
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| 130 |
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run: |
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| 131 |
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python -m pip install --upgrade pip
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| 132 |
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pip install .
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| 133 |
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| 134 |
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- name: Run test
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| 135 |
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run:
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python main.py -o config_files/options/baseline.json -a config_files/algorithm/evaluation_plotter.json
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| 138 |
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test_integration:
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| 139 |
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runs-on: ubuntu-latest
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| 140 |
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| 141 |
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# Setting up a python envronment for the test script to run
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| 142 |
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steps:
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| 143 |
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- name: Checkout code
|
| 144 |
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uses: actions/checkout@v4
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| 145 |
+
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| 146 |
+
- name: Set up Python
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| 147 |
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uses: actions/setup-python@v5
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| 148 |
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with:
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| 149 |
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python-version: 3.9
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| 150 |
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| 151 |
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- name: Install dependencies
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run: |
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sudo apt-get install build-essential python3 python3-dev
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- name: Install feeed
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run: |
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python -m pip install --upgrade pip
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pip install .
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- name: Run test
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run:
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python main.py -o config_files/options/baseline.json -a config_files/algorithm/experiment_test.json
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.gitignore
CHANGED
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smac3_output/
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data/
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output/
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smac3_output/
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data/
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output/
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.ipynb_checkpoints/
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notebooks/.ipynb_checkpoints/*
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gedi.egg-info/
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build/
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README.md
CHANGED
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@@ -16,12 +16,11 @@ For MacOS:
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brew install graphviz
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brew install swig
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```
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-
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-
## Installation
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- For smac:
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```console
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conda install pyrfr swig
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```
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- `conda env create -f .conda.yml`
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- Install [Feature Extractor for Event Data (feeed)](https://github.com/lmu-dbs/feeed) in the newly installed conda environment: `pip install feeed`
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@@ -33,7 +32,7 @@ python main.py -o config_files/options/baseline.json -a config_files/algorithm/e
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## Usage
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Our pipeline offers several pipeline steps, which can be run sequentially or partially:
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- feature_extraction
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| 36 |
-
-
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- benchmark
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| 38 |
- evaluation_plotter
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brew install graphviz
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brew install swig
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```
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- For smac:
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```console
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conda install pyrfr swig
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```
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| 23 |
+
## Installation
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| 24 |
- `conda env create -f .conda.yml`
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- Install [Feature Extractor for Event Data (feeed)](https://github.com/lmu-dbs/feeed) in the newly installed conda environment: `pip install feeed`
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| 26 |
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| 32 |
## Usage
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| 33 |
Our pipeline offers several pipeline steps, which can be run sequentially or partially:
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| 34 |
- feature_extraction
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| 35 |
+
- generation
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| 36 |
- benchmark
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| 37 |
- evaluation_plotter
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| 38 |
|
config.py
CHANGED
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@@ -2,7 +2,7 @@ import json
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| 2 |
import os
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import warnings
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-
from
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from tqdm import tqdm
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from utils.param_keys import INPUT_NAME, FILENAME, FOLDER_PATH, PARAMS
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@@ -63,8 +63,8 @@ def get_files_and_kwargs(params: dict):
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#TODO: generate parent directories if they don't exist
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if input_name == 'test':
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-
filename_list = list(tqdm(sort_files(os.listdir('data/
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-
kwargs = {FILENAME: filename_list, FOLDER_PATH: 'data/
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elif input_name == 'realLogs':
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filename_list = list(tqdm(sort_files(os.listdir('data/real_event_logs'))))
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kwargs = {FILENAME: filename_list, FOLDER_PATH: 'data/real_event_logs'}
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import os
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import warnings
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from gedi.utils.io_helpers import sort_files
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from tqdm import tqdm
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from utils.param_keys import INPUT_NAME, FILENAME, FOLDER_PATH, PARAMS
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#TODO: generate parent directories if they don't exist
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if input_name == 'test':
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filename_list = list(tqdm(sort_files(os.listdir('data/test'))))
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kwargs = {FILENAME: filename_list, FOLDER_PATH: 'data/test'}
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elif input_name == 'realLogs':
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filename_list = list(tqdm(sort_files(os.listdir('data/real_event_logs'))))
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kwargs = {FILENAME: filename_list, FOLDER_PATH: 'data/real_event_logs'}
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config_files/algorithm/benchmark.json
CHANGED
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{
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"pipeline_step": "benchmark_test",
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"benchmark_test": "discovery",
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-
"input_path":"data/
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-
"input_path":"data/test_2/gen_el_168.xes",
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"output_path":"output",
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"miners" : ["inductive", "heuristics", "imf", "ilp"]
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}
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{
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"pipeline_step": "benchmark_test",
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"benchmark_test": "discovery",
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"input_path":"data/test",
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"output_path":"output",
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"miners" : ["inductive", "heuristics", "imf", "ilp"]
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}
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config_files/algorithm/evaluation_plotter.json
CHANGED
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@@ -2,16 +2,18 @@
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{
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"pipeline_step": "evaluation_plotter",
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"input_path": "output/features/generated/34_bpic_features/",
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-
"input_path": "output/features/generated/grid_1obj/1_enve_feat.csv",
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"input_path": "output/features/generated/grid_2obj/",
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"input_path": ["output/features/generated/grid_1obj/", "output/features/generated/grid_2obj/"],
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-
"
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"reference_feature": "epa_normalized_sequence_entropy",
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-
"reference_feature": "epa_normalized_variant_entropy",
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"reference_feature": "epa_normalized_sequence_entropy_exponential_forgetting",
|
|
|
|
| 12 |
"targets": "data/34_bpic_features.csv",
|
| 13 |
-
"targets": "data/grid_experiments/grid_1obj/grid_1objectives_enve.csv",
|
| 14 |
"targets": "data/grid_experiments/grid_2obj/",
|
| 15 |
-
"targets": ["data/grid_experiments/grid_1obj/", "data/grid_experiments/grid_2obj/"]
|
|
|
|
|
|
|
|
|
|
| 16 |
}
|
| 17 |
]
|
|
|
|
| 2 |
{
|
| 3 |
"pipeline_step": "evaluation_plotter",
|
| 4 |
"input_path": "output/features/generated/34_bpic_features/",
|
|
|
|
| 5 |
"input_path": "output/features/generated/grid_2obj/",
|
| 6 |
"input_path": ["output/features/generated/grid_1obj/", "output/features/generated/grid_2obj/"],
|
| 7 |
+
"input_path": "output/features/generated/grid_1obj/1_enve_feat.csv",
|
| 8 |
+
"input_path": "data/test/plotter/1_enve_feat.csv",
|
| 9 |
"reference_feature": "epa_normalized_sequence_entropy",
|
|
|
|
| 10 |
"reference_feature": "epa_normalized_sequence_entropy_exponential_forgetting",
|
| 11 |
+
"reference_feature": "epa_normalized_variant_entropy",
|
| 12 |
"targets": "data/34_bpic_features.csv",
|
|
|
|
| 13 |
"targets": "data/grid_experiments/grid_2obj/",
|
| 14 |
+
"targets": ["data/grid_experiments/grid_1obj/", "data/grid_experiments/grid_2obj/"],
|
| 15 |
+
"targets": "data/grid_experiments/grid_1obj/grid_1objectives_enve.csv",
|
| 16 |
+
"targets": "data/test/plotter/grid_1objectives_enve.csv",
|
| 17 |
+
"output_path": "output/plots"
|
| 18 |
}
|
| 19 |
]
|
config_files/algorithm/experiment_test.json
CHANGED
|
@@ -9,16 +9,16 @@
|
|
| 9 |
{
|
| 10 |
"pipeline_step": "event_logs_generation",
|
| 11 |
"output_path": "output/features/2_bpic_features/2_ense_rmcv_feat.csv",
|
| 12 |
-
"output_path": "data/
|
| 13 |
"generator_params": {
|
| 14 |
"experiment": "data/grid_objectives.csv",
|
| 15 |
"experiment": {"input_path": "data/2_bpic_features.csv",
|
| 16 |
"objectives": ["ratio_top_20_variants", "epa_normalized_sequence_entropy_linear_forgetting"]},
|
| 17 |
"experiment": [
|
| 18 |
-
{"epa_normalized_sequence_entropy_linear_forgetting": 0.
|
| 19 |
-
{"epa_normalized_sequence_entropy_linear_forgetting": 0.
|
| 20 |
],
|
| 21 |
-
"experiment": {"epa_normalized_sequence_entropy_linear_forgetting": 0.
|
| 22 |
"config_space": {
|
| 23 |
"mode": [5, 20],
|
| 24 |
"sequence": [0.01, 1],
|
|
@@ -27,7 +27,7 @@
|
|
| 27 |
"loop": [0.01, 1],
|
| 28 |
"silent": [0.01, 1],
|
| 29 |
"lt_dependency": [0.01, 1],
|
| 30 |
-
"num_traces": [
|
| 31 |
"duplicate": [0],
|
| 32 |
"or": [0]
|
| 33 |
},
|
|
@@ -36,7 +36,7 @@
|
|
| 36 |
},
|
| 37 |
{
|
| 38 |
"pipeline_step": "feature_extraction",
|
| 39 |
-
"input_path": "data/
|
| 40 |
"feature_params": {"feature_set":["trace_length"]},
|
| 41 |
"output_path": "output/plots",
|
| 42 |
"real_eventlog_path": "data/bpic_features.csv",
|
|
@@ -45,7 +45,7 @@
|
|
| 45 |
{
|
| 46 |
"pipeline_step": "benchmark_test",
|
| 47 |
"benchmark_test": "discovery",
|
| 48 |
-
"input_path":"data/
|
| 49 |
"output_path":"output",
|
| 50 |
"miners" : ["inductive", "heuristics", "imf", "ilp"]
|
| 51 |
}
|
|
|
|
| 9 |
{
|
| 10 |
"pipeline_step": "event_logs_generation",
|
| 11 |
"output_path": "output/features/2_bpic_features/2_ense_rmcv_feat.csv",
|
| 12 |
+
"output_path": "data/test",
|
| 13 |
"generator_params": {
|
| 14 |
"experiment": "data/grid_objectives.csv",
|
| 15 |
"experiment": {"input_path": "data/2_bpic_features.csv",
|
| 16 |
"objectives": ["ratio_top_20_variants", "epa_normalized_sequence_entropy_linear_forgetting"]},
|
| 17 |
"experiment": [
|
| 18 |
+
{"epa_normalized_sequence_entropy_linear_forgetting": 0.2, "ratio_top_20_variants": 0.4},
|
| 19 |
+
{"epa_normalized_sequence_entropy_linear_forgetting": 0.4, "ratio_top_20_variants": 0.7}
|
| 20 |
],
|
| 21 |
+
"experiment": {"epa_normalized_sequence_entropy_linear_forgetting": 0.2, "ratio_top_20_variants": 0.4},
|
| 22 |
"config_space": {
|
| 23 |
"mode": [5, 20],
|
| 24 |
"sequence": [0.01, 1],
|
|
|
|
| 27 |
"loop": [0.01, 1],
|
| 28 |
"silent": [0.01, 1],
|
| 29 |
"lt_dependency": [0.01, 1],
|
| 30 |
+
"num_traces": [10, 100],
|
| 31 |
"duplicate": [0],
|
| 32 |
"or": [0]
|
| 33 |
},
|
|
|
|
| 36 |
},
|
| 37 |
{
|
| 38 |
"pipeline_step": "feature_extraction",
|
| 39 |
+
"input_path": "data/test",
|
| 40 |
"feature_params": {"feature_set":["trace_length"]},
|
| 41 |
"output_path": "output/plots",
|
| 42 |
"real_eventlog_path": "data/bpic_features.csv",
|
|
|
|
| 45 |
{
|
| 46 |
"pipeline_step": "benchmark_test",
|
| 47 |
"benchmark_test": "discovery",
|
| 48 |
+
"input_path":"data/test",
|
| 49 |
"output_path":"output",
|
| 50 |
"miners" : ["inductive", "heuristics", "imf", "ilp"]
|
| 51 |
}
|
config_files/algorithm/feature_extraction.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
[
|
| 2 |
{
|
| 3 |
"pipeline_step": "feature_extraction",
|
| 4 |
-
"input_path": "data/
|
| 5 |
"feature_params": {"feature_set":["simple_stats", "trace_length", "trace_variant", "activities", "start_activities", "end_activities", "eventropies", "epa_based"]},
|
| 6 |
"output_path": "output/plots",
|
| 7 |
"real_eventlog_path": "data/bpic_features.csv",
|
|
|
|
| 1 |
[
|
| 2 |
{
|
| 3 |
"pipeline_step": "feature_extraction",
|
| 4 |
+
"input_path": "data/test",
|
| 5 |
"feature_params": {"feature_set":["simple_stats", "trace_length", "trace_variant", "activities", "start_activities", "end_activities", "eventropies", "epa_based"]},
|
| 6 |
"output_path": "output/plots",
|
| 7 |
"real_eventlog_path": "data/bpic_features.csv",
|
config_files/algorithm/generation.json
CHANGED
|
@@ -3,11 +3,8 @@
|
|
| 3 |
"pipeline_step": "event_logs_generation",
|
| 4 |
"output_path": "output",
|
| 5 |
"generator_params": {
|
| 6 |
-
"experiment": {
|
| 7 |
-
"
|
| 8 |
-
"objectives": ["epa_normalized_variant_entropy"],
|
| 9 |
-
"objectives": ["ratio_most_common_variant", "epa_normalized_sequence_entropy"],
|
| 10 |
-
"objectives": ["ratio_top_20_variants","epa_normalized_sequence_entropy_linear_forgetting"]
|
| 11 |
},
|
| 12 |
"config_space": {
|
| 13 |
"mode": [5, 20],
|
|
|
|
| 3 |
"pipeline_step": "event_logs_generation",
|
| 4 |
"output_path": "output",
|
| 5 |
"generator_params": {
|
| 6 |
+
"experiment": {"input_path": "data/test/grid_feat.csv",
|
| 7 |
+
"objectives": ["ratio_top_20_variants", "epa_normalized_sequence_entropy_linear_forgetting"]
|
|
|
|
|
|
|
|
|
|
| 8 |
},
|
| 9 |
"config_space": {
|
| 10 |
"mode": [5, 20],
|
data/2_grid_test.csv
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
-
|
| 2 |
-
|
| 3 |
-
|
|
|
|
| 1 |
+
log,ratio_top_20_variants,epa_normalized_sequence_entropy_linear_forgetting
|
| 2 |
+
experiment1,0.2,0.4
|
| 3 |
+
experiment2,0.4,0.7
|
data/{test_2 → test}/gen_el_168.xes
RENAMED
|
File without changes
|
data/{test_2 → test}/gen_el_169.xes
RENAMED
|
File without changes
|
data/test/grid_feat.csv
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
log,ratio_top_20_variants,epa_normalized_sequence_entropy_linear_forgetting
|
| 2 |
+
experiment1,0.2,0.4
|
| 3 |
+
experiment2,0.4,0.7
|
data/test/plotter/1_enve_feat.csv
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
epa_normalized_variant_entropy,log
|
| 2 |
+
0.41202322946059605,task_5
|
| 3 |
+
0.79999386158591,task_9
|
| 4 |
+
0.8925919422394111,task_10
|
| 5 |
+
0.493812449168448,task_6
|
| 6 |
+
0.20299577565110202,task_3
|
| 7 |
+
0.337263992015401,task_4
|
| 8 |
+
0.0,task_1
|
| 9 |
+
0.102184538023266,task_2
|
| 10 |
+
0.600006599245775,task_7
|
| 11 |
+
0.6999779396851361,task_8
|
| 12 |
+
0.8796185572534461,task_11
|
data/test/plotter/grid_1objectives_enve.csv
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
task,epa_normalized_variant_entropy
|
| 2 |
+
task_1,0.0
|
| 3 |
+
task_2,0.1
|
| 4 |
+
task_3,0.2
|
| 5 |
+
task_4,0.3
|
| 6 |
+
task_5,0.4
|
| 7 |
+
task_6,0.5
|
| 8 |
+
task_7,0.6
|
| 9 |
+
task_8,0.7
|
| 10 |
+
task_9,0.8
|
| 11 |
+
task_10,0.9
|
| 12 |
+
task_11,1.0
|
execute_grid_experiments.py
CHANGED
|
@@ -2,7 +2,7 @@ import multiprocessing
|
|
| 2 |
import os
|
| 3 |
|
| 4 |
from datetime import datetime as dt
|
| 5 |
-
from
|
| 6 |
from tqdm import tqdm
|
| 7 |
|
| 8 |
#TODO: Pass i properly
|
|
|
|
| 2 |
import os
|
| 3 |
|
| 4 |
from datetime import datetime as dt
|
| 5 |
+
from gedi.utils.io_helpers import sort_files
|
| 6 |
from tqdm import tqdm
|
| 7 |
|
| 8 |
#TODO: Pass i properly
|
gedi/__init__.py
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from .generator import GenerateEventLogs
|
| 2 |
+
from .features import EventLogFeatures
|
| 3 |
+
from .analyser import FeatureAnalyser
|
| 4 |
+
from .augmentation import InstanceAugmentator
|
| 5 |
+
from .benchmark import BenchmarkTest
|
| 6 |
+
from .plotter import BenchmarkPlotter, FeaturesPlotter, AugmentationPlotter, GenerationPlotter
|
| 7 |
+
|
| 8 |
+
__all__=[ 'GenerateEventLogs', 'EventLogFeatures', 'FeatureAnalyser', 'InstanceAugmentator', 'BenchmarkTest', 'BenchmarkPlotter', 'FeaturesPlotter', 'AugmentationPlotter', 'GenerationPlotter']
|
{tag → gedi}/analyser.py
RENAMED
|
@@ -4,9 +4,9 @@ import warnings
|
|
| 4 |
from sklearn.decomposition import FastICA, PCA
|
| 5 |
from sklearn.manifold import TSNE
|
| 6 |
from sklearn.preprocessing import Normalizer, StandardScaler
|
| 7 |
-
from
|
| 8 |
-
from
|
| 9 |
-
from
|
| 10 |
# TODO: Call param_keys explicitly e.g. import INPUT_PATH
|
| 11 |
from utils.param_keys import *
|
| 12 |
from utils.param_keys.analyser import MODEL, INPUT_PARAMS, PERPLEXITY
|
|
|
|
| 4 |
from sklearn.decomposition import FastICA, PCA
|
| 5 |
from sklearn.manifold import TSNE
|
| 6 |
from sklearn.preprocessing import Normalizer, StandardScaler
|
| 7 |
+
from gedi.features import EventLogFeatures
|
| 8 |
+
from gedi.plotter import ModelResultPlotter
|
| 9 |
+
from gedi.utils.matrix_tools import insert_missing_data
|
| 10 |
# TODO: Call param_keys explicitly e.g. import INPUT_PATH
|
| 11 |
from utils.param_keys import *
|
| 12 |
from utils.param_keys.analyser import MODEL, INPUT_PARAMS, PERPLEXITY
|
{tag → gedi}/augmentation.py
RENAMED
|
@@ -3,7 +3,7 @@ from collections import Counter
|
|
| 3 |
from datetime import datetime as dt
|
| 4 |
from imblearn.over_sampling import SMOTE, SVMSMOTE, BorderlineSMOTE, KMeansSMOTE
|
| 5 |
from sklearn.preprocessing import Normalizer
|
| 6 |
-
from
|
| 7 |
from utils.param_keys import INPUT_PATH, OUTPUT_PATH
|
| 8 |
from utils.param_keys.augmentation import AUGMENTATION_PARAMS, NO_SAMPLES, FEATURE_SELECTION, METHOD
|
| 9 |
|
|
|
|
| 3 |
from datetime import datetime as dt
|
| 4 |
from imblearn.over_sampling import SMOTE, SVMSMOTE, BorderlineSMOTE, KMeansSMOTE
|
| 5 |
from sklearn.preprocessing import Normalizer
|
| 6 |
+
from gedi.utils.matrix_tools import insert_missing_data
|
| 7 |
from utils.param_keys import INPUT_PATH, OUTPUT_PATH
|
| 8 |
from utils.param_keys.augmentation import AUGMENTATION_PARAMS, NO_SAMPLES, FEATURE_SELECTION, METHOD
|
| 9 |
|
{tag → gedi}/benchmark.py
RENAMED
|
@@ -16,7 +16,7 @@ from pm4py.algo.evaluation.generalization import algorithm as generalization_eva
|
|
| 16 |
from pm4py.algo.evaluation.simplicity import algorithm as simplicity_evaluator
|
| 17 |
from pm4py.objects.bpmn.obj import BPMN
|
| 18 |
from pm4py.objects.log.importer.xes import importer as xes_importer
|
| 19 |
-
from
|
| 20 |
from tqdm import tqdm
|
| 21 |
from utils.param_keys import INPUT_PATH, OUTPUT_PATH
|
| 22 |
from utils.param_keys.benchmark import MINERS
|
|
@@ -113,14 +113,14 @@ class BenchmarkTest:
|
|
| 113 |
return
|
| 114 |
|
| 115 |
def split_miner_wrapper(self, log_path="data/real_event_logs/BPI_Challenges/BPI_Challenge_2012.xes"):
|
| 116 |
-
jar_path = os.path.join("
|
| 117 |
filename = os.path.split(log_path)[-1].rsplit(".",1)[0]
|
| 118 |
bpmn_path = os.path.join("output", "bpmns_split", filename)
|
| 119 |
os.makedirs(os.path.split(bpmn_path)[0], exist_ok=True)
|
| 120 |
command = [
|
| 121 |
"java",
|
| 122 |
"-cp",
|
| 123 |
-
f"{os.getcwd()}/
|
| 124 |
"au.edu.unimelb.services.ServiceProvider",
|
| 125 |
"SM2",
|
| 126 |
f"{os.getcwd()}/{log_path}",
|
|
|
|
| 16 |
from pm4py.algo.evaluation.simplicity import algorithm as simplicity_evaluator
|
| 17 |
from pm4py.objects.bpmn.obj import BPMN
|
| 18 |
from pm4py.objects.log.importer.xes import importer as xes_importer
|
| 19 |
+
from gedi.utils.io_helpers import dump_features_json
|
| 20 |
from tqdm import tqdm
|
| 21 |
from utils.param_keys import INPUT_PATH, OUTPUT_PATH
|
| 22 |
from utils.param_keys.benchmark import MINERS
|
|
|
|
| 113 |
return
|
| 114 |
|
| 115 |
def split_miner_wrapper(self, log_path="data/real_event_logs/BPI_Challenges/BPI_Challenge_2012.xes"):
|
| 116 |
+
jar_path = os.path.join("gedi","libs","split-miner-1.7.1-all.jar")
|
| 117 |
filename = os.path.split(log_path)[-1].rsplit(".",1)[0]
|
| 118 |
bpmn_path = os.path.join("output", "bpmns_split", filename)
|
| 119 |
os.makedirs(os.path.split(bpmn_path)[0], exist_ok=True)
|
| 120 |
command = [
|
| 121 |
"java",
|
| 122 |
"-cp",
|
| 123 |
+
f"{os.getcwd()}/gedi/libs/sm2.jar:{os.getcwd()}/tag/libs/lib/*",
|
| 124 |
"au.edu.unimelb.services.ServiceProvider",
|
| 125 |
"SM2",
|
| 126 |
f"{os.getcwd()}/{log_path}",
|
{tag → gedi}/features.py
RENAMED
|
@@ -11,7 +11,7 @@ from pathlib import Path, PurePath
|
|
| 11 |
from sklearn.impute import SimpleImputer
|
| 12 |
from utils.param_keys import INPUT_PATH
|
| 13 |
from utils.param_keys.features import FEATURE_PARAMS, FEATURE_SET
|
| 14 |
-
from
|
| 15 |
|
| 16 |
def get_sortby_parameter(elem):
|
| 17 |
number = int(elem.rsplit(".")[0].rsplit("_", 1)[1])
|
|
|
|
| 11 |
from sklearn.impute import SimpleImputer
|
| 12 |
from utils.param_keys import INPUT_PATH
|
| 13 |
from utils.param_keys.features import FEATURE_PARAMS, FEATURE_SET
|
| 14 |
+
from gedi.utils.io_helpers import dump_features_json
|
| 15 |
|
| 16 |
def get_sortby_parameter(elem):
|
| 17 |
number = int(elem.rsplit(".")[0].rsplit("_", 1)[1])
|
{tag → gedi}/generator.py
RENAMED
|
@@ -20,7 +20,7 @@ from pm4py.sim import play_out
|
|
| 20 |
from smac import HyperparameterOptimizationFacade, Scenario
|
| 21 |
from utils.param_keys import OUTPUT_PATH, INPUT_PATH
|
| 22 |
from utils.param_keys.generator import GENERATOR_PARAMS, EXPERIMENT, CONFIG_SPACE, N_TRIALS
|
| 23 |
-
from
|
| 24 |
|
| 25 |
|
| 26 |
|
|
@@ -73,7 +73,7 @@ def get_tasks(experiment, output_path="", reference_feature=None):
|
|
| 73 |
return tasks, output_path
|
| 74 |
|
| 75 |
class GenerateEventLogs():
|
| 76 |
-
# TODO: Clarify nomenclature: experiment, task, objective as in notebook (https://github.com/lmu-dbs/
|
| 77 |
def __init__(self, params):
|
| 78 |
print("=========================== Generator ==========================")
|
| 79 |
print(f"INFO: Running with {params}")
|
|
|
|
| 20 |
from smac import HyperparameterOptimizationFacade, Scenario
|
| 21 |
from utils.param_keys import OUTPUT_PATH, INPUT_PATH
|
| 22 |
from utils.param_keys.generator import GENERATOR_PARAMS, EXPERIMENT, CONFIG_SPACE, N_TRIALS
|
| 23 |
+
from gedi.utils.io_helpers import get_output_key_value_location, dump_features_json, read_csvs
|
| 24 |
|
| 25 |
|
| 26 |
|
|
|
|
| 73 |
return tasks, output_path
|
| 74 |
|
| 75 |
class GenerateEventLogs():
|
| 76 |
+
# TODO: Clarify nomenclature: experiment, task, objective as in notebook (https://github.com/lmu-dbs/gedi/blob/main/notebooks/grid_objectives.ipynb)
|
| 77 |
def __init__(self, params):
|
| 78 |
print("=========================== Generator ==========================")
|
| 79 |
print(f"INFO: Running with {params}")
|
{tag → gedi}/plotter.py
RENAMED
|
@@ -20,9 +20,9 @@ from collections import defaultdict
|
|
| 20 |
from sklearn.preprocessing import Normalizer, StandardScaler
|
| 21 |
from sklearn.decomposition import PCA
|
| 22 |
from sklearn.metrics.pairwise import euclidean_distances
|
| 23 |
-
from
|
| 24 |
-
from
|
| 25 |
-
from
|
| 26 |
|
| 27 |
def insert_newlines(string, every=140):
|
| 28 |
return '\n'.join(string[i:i+every] for i in range(0, len(string), every))
|
|
@@ -331,6 +331,7 @@ class FeaturesPlotter:
|
|
| 331 |
fig, output_path = eval(f"self.plot_violinplot_single(features, output_path, source='{source_name}' {plot_type})")
|
| 332 |
|
| 333 |
if output_path != None:
|
|
|
|
| 334 |
fig.savefig(output_path)
|
| 335 |
print(f"SUCCESS: Saved {plot_type} plot in {output_path}")
|
| 336 |
|
|
@@ -617,7 +618,7 @@ class AugmentationPlotter(object):
|
|
| 617 |
if output_path != None:
|
| 618 |
output_path += f"/augmentation_pca_{n_features}_{self.sampler}.jpg"
|
| 619 |
fig.savefig(output_path)
|
| 620 |
-
print("SUCCESS: Saved augmentation pca plot at {output_path}")
|
| 621 |
|
| 622 |
|
| 623 |
class GenerationPlotter(object):
|
|
@@ -672,7 +673,7 @@ class GenerationPlotter(object):
|
|
| 672 |
targets = orig_targets.copy()
|
| 673 |
elif isinstance(orig_targets, defaultdict):
|
| 674 |
if k not in orig_targets:
|
| 675 |
-
print("[WARNING] {k} not in targets. Only in generated features. Will continue with next feature to compare with")
|
| 676 |
continue
|
| 677 |
targets = orig_targets[k].copy()
|
| 678 |
else:
|
|
|
|
| 20 |
from sklearn.preprocessing import Normalizer, StandardScaler
|
| 21 |
from sklearn.decomposition import PCA
|
| 22 |
from sklearn.metrics.pairwise import euclidean_distances
|
| 23 |
+
from gedi.generator import get_tasks
|
| 24 |
+
from gedi.utils.io_helpers import get_keys_abbreviation
|
| 25 |
+
from gedi.utils.io_helpers import read_csvs, select_instance
|
| 26 |
|
| 27 |
def insert_newlines(string, every=140):
|
| 28 |
return '\n'.join(string[i:i+every] for i in range(0, len(string), every))
|
|
|
|
| 331 |
fig, output_path = eval(f"self.plot_violinplot_single(features, output_path, source='{source_name}' {plot_type})")
|
| 332 |
|
| 333 |
if output_path != None:
|
| 334 |
+
os.makedirs(os.path.split(output_path)[0], exist_ok=True)
|
| 335 |
fig.savefig(output_path)
|
| 336 |
print(f"SUCCESS: Saved {plot_type} plot in {output_path}")
|
| 337 |
|
|
|
|
| 618 |
if output_path != None:
|
| 619 |
output_path += f"/augmentation_pca_{n_features}_{self.sampler}.jpg"
|
| 620 |
fig.savefig(output_path)
|
| 621 |
+
print(f"SUCCESS: Saved augmentation pca plot at {output_path}")
|
| 622 |
|
| 623 |
|
| 624 |
class GenerationPlotter(object):
|
|
|
|
| 673 |
targets = orig_targets.copy()
|
| 674 |
elif isinstance(orig_targets, defaultdict):
|
| 675 |
if k not in orig_targets:
|
| 676 |
+
print(f"[WARNING] {k} not in targets. Only in generated features. Will continue with next feature to compare with")
|
| 677 |
continue
|
| 678 |
targets = orig_targets[k].copy()
|
| 679 |
else:
|
{tag → gedi}/utils/algorithms/__init__.py
RENAMED
|
File without changes
|
{tag → gedi}/utils/algorithms/tsne.py
RENAMED
|
File without changes
|
{tag → gedi}/utils/array_tools.py
RENAMED
|
File without changes
|
{tag → gedi}/utils/io_helpers.py
RENAMED
|
File without changes
|
{tag → gedi}/utils/matrix_tools.py
RENAMED
|
File without changes
|
main.py
CHANGED
|
@@ -1,12 +1,12 @@
|
|
| 1 |
import config
|
| 2 |
import pandas as pd
|
| 3 |
from datetime import datetime as dt
|
| 4 |
-
from
|
| 5 |
-
from
|
| 6 |
-
from
|
| 7 |
-
from
|
| 8 |
-
from
|
| 9 |
-
from
|
| 10 |
from utils.default_argparse import ArgParser
|
| 11 |
from utils.param_keys import *
|
| 12 |
from utils.param_keys.run_options import *
|
|
@@ -57,8 +57,8 @@ def run(kwargs:dict, model_paramas_list: list, filename_list:list):
|
|
| 57 |
|
| 58 |
|
| 59 |
if __name__=='__main__':
|
| 60 |
-
|
| 61 |
-
print(f'INFO:
|
| 62 |
|
| 63 |
args = ArgParser().parse('GEDI main')
|
| 64 |
run_params = config.get_run_params(args.run_params_json)
|
|
@@ -70,4 +70,4 @@ if __name__=='__main__':
|
|
| 70 |
else:
|
| 71 |
load(args.result_load_files, kwargs)
|
| 72 |
|
| 73 |
-
print(f'SUCCESS:
|
|
|
|
| 1 |
import config
|
| 2 |
import pandas as pd
|
| 3 |
from datetime import datetime as dt
|
| 4 |
+
from gedi.generator import GenerateEventLogs
|
| 5 |
+
from gedi.features import EventLogFeatures
|
| 6 |
+
from gedi.analyser import FeatureAnalyser
|
| 7 |
+
from gedi.augmentation import InstanceAugmentator
|
| 8 |
+
from gedi.benchmark import BenchmarkTest
|
| 9 |
+
from gedi.plotter import BenchmarkPlotter, FeaturesPlotter, AugmentationPlotter, GenerationPlotter
|
| 10 |
from utils.default_argparse import ArgParser
|
| 11 |
from utils.param_keys import *
|
| 12 |
from utils.param_keys.run_options import *
|
|
|
|
| 57 |
|
| 58 |
|
| 59 |
if __name__=='__main__':
|
| 60 |
+
start_gedi = dt.now()
|
| 61 |
+
print(f'INFO: GEDI starting {start_gedi}')
|
| 62 |
|
| 63 |
args = ArgParser().parse('GEDI main')
|
| 64 |
run_params = config.get_run_params(args.run_params_json)
|
|
|
|
| 70 |
else:
|
| 71 |
load(args.result_load_files, kwargs)
|
| 72 |
|
| 73 |
+
print(f'SUCCESS: GEDI took {dt.now()-start_gedi} sec.')
|
notebooks/.ipynb_checkpoints/augmentation-checkpoint.ipynb
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|
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|
|
|
notebooks/.ipynb_checkpoints/benchmarking_process_discovery-checkpoint.ipynb
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|
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|
|
|
notebooks/.ipynb_checkpoints/bpic_generability_pdm-checkpoint.ipynb
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|
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|
|
notebooks/.ipynb_checkpoints/data_exploration-checkpoint.ipynb
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|
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|
|
|
notebooks/.ipynb_checkpoints/experiment_generator-checkpoint.ipynb
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|
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|
|
|
notebooks/.ipynb_checkpoints/feature_distributions-checkpoint.ipynb
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|
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|
|
|
notebooks/.ipynb_checkpoints/feature_exploration-checkpoint.ipynb
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|
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|
|
|
notebooks/.ipynb_checkpoints/feature_performance_similarity-checkpoint.ipynb
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|
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|
|
|
notebooks/.ipynb_checkpoints/feature_selection-checkpoint.ipynb
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|
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|
|
|
notebooks/.ipynb_checkpoints/feature_variance-checkpoint.ipynb
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|
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|
|
|
notebooks/.ipynb_checkpoints/gedi_representativeness-checkpoint.ipynb
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|
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|
|
|
notebooks/.ipynb_checkpoints/grid_objectives-checkpoint.ipynb
DELETED
|
@@ -1,376 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"cells": [
|
| 3 |
-
{
|
| 4 |
-
"cell_type": "code",
|
| 5 |
-
"execution_count": 9,
|
| 6 |
-
"id": "e5aa7223",
|
| 7 |
-
"metadata": {},
|
| 8 |
-
"outputs": [],
|
| 9 |
-
"source": [
|
| 10 |
-
"import pandas as pd\n",
|
| 11 |
-
"import numpy as np"
|
| 12 |
-
]
|
| 13 |
-
},
|
| 14 |
-
{
|
| 15 |
-
"cell_type": "code",
|
| 16 |
-
"execution_count": 10,
|
| 17 |
-
"id": "dfd1a302",
|
| 18 |
-
"metadata": {},
|
| 19 |
-
"outputs": [],
|
| 20 |
-
"source": [
|
| 21 |
-
"df = pd.DataFrame(columns=[\"log\",\"ratio_top_20_variants\", \"normalized_sequence_entropy_linear_forgetting\"]) "
|
| 22 |
-
]
|
| 23 |
-
},
|
| 24 |
-
{
|
| 25 |
-
"cell_type": "code",
|
| 26 |
-
"execution_count": 28,
|
| 27 |
-
"id": "218946b7",
|
| 28 |
-
"metadata": {},
|
| 29 |
-
"outputs": [],
|
| 30 |
-
"source": [
|
| 31 |
-
"k=0\n",
|
| 32 |
-
"for i in np.arange(0.2, 1.1,0.2):\n",
|
| 33 |
-
" for j in np.arange(0,0.55,0.1):\n",
|
| 34 |
-
" k+=1\n",
|
| 35 |
-
" new_entry = pd.Series({'log':f\"objective_{k}\", \"ratio_top_20_variants\":round(i,1),\n",
|
| 36 |
-
" \"normalized_sequence_entropy_linear_forgetting\":round(j,1)})\n",
|
| 37 |
-
" df = pd.concat([\n",
|
| 38 |
-
" df, \n",
|
| 39 |
-
" pd.DataFrame([new_entry], columns=new_entry.index)]\n",
|
| 40 |
-
" ).reset_index(drop=True)\n",
|
| 41 |
-
" "
|
| 42 |
-
]
|
| 43 |
-
},
|
| 44 |
-
{
|
| 45 |
-
"cell_type": "code",
|
| 46 |
-
"execution_count": 31,
|
| 47 |
-
"id": "b1e3bb5a",
|
| 48 |
-
"metadata": {},
|
| 49 |
-
"outputs": [],
|
| 50 |
-
"source": [
|
| 51 |
-
"df.to_csv(\"../data/grid_objectives.csv\" ,index=False)"
|
| 52 |
-
]
|
| 53 |
-
},
|
| 54 |
-
{
|
| 55 |
-
"cell_type": "code",
|
| 56 |
-
"execution_count": 32,
|
| 57 |
-
"id": "5de45389",
|
| 58 |
-
"metadata": {},
|
| 59 |
-
"outputs": [
|
| 60 |
-
{
|
| 61 |
-
"data": {
|
| 62 |
-
"text/html": [
|
| 63 |
-
"<div>\n",
|
| 64 |
-
"<style scoped>\n",
|
| 65 |
-
" .dataframe tbody tr th:only-of-type {\n",
|
| 66 |
-
" vertical-align: middle;\n",
|
| 67 |
-
" }\n",
|
| 68 |
-
"\n",
|
| 69 |
-
" .dataframe tbody tr th {\n",
|
| 70 |
-
" vertical-align: top;\n",
|
| 71 |
-
" }\n",
|
| 72 |
-
"\n",
|
| 73 |
-
" .dataframe thead th {\n",
|
| 74 |
-
" text-align: right;\n",
|
| 75 |
-
" }\n",
|
| 76 |
-
"</style>\n",
|
| 77 |
-
"<table border=\"1\" class=\"dataframe\">\n",
|
| 78 |
-
" <thead>\n",
|
| 79 |
-
" <tr style=\"text-align: right;\">\n",
|
| 80 |
-
" <th></th>\n",
|
| 81 |
-
" <th>log</th>\n",
|
| 82 |
-
" <th>ratio_top_20_variants</th>\n",
|
| 83 |
-
" <th>normalized_sequence_entropy_linear_forgetting</th>\n",
|
| 84 |
-
" </tr>\n",
|
| 85 |
-
" </thead>\n",
|
| 86 |
-
" <tbody>\n",
|
| 87 |
-
" <tr>\n",
|
| 88 |
-
" <th>0</th>\n",
|
| 89 |
-
" <td>objective_1</td>\n",
|
| 90 |
-
" <td>0.2</td>\n",
|
| 91 |
-
" <td>0.0</td>\n",
|
| 92 |
-
" </tr>\n",
|
| 93 |
-
" <tr>\n",
|
| 94 |
-
" <th>1</th>\n",
|
| 95 |
-
" <td>objective_2</td>\n",
|
| 96 |
-
" <td>0.2</td>\n",
|
| 97 |
-
" <td>0.1</td>\n",
|
| 98 |
-
" </tr>\n",
|
| 99 |
-
" <tr>\n",
|
| 100 |
-
" <th>2</th>\n",
|
| 101 |
-
" <td>objective_3</td>\n",
|
| 102 |
-
" <td>0.2</td>\n",
|
| 103 |
-
" <td>0.2</td>\n",
|
| 104 |
-
" </tr>\n",
|
| 105 |
-
" <tr>\n",
|
| 106 |
-
" <th>3</th>\n",
|
| 107 |
-
" <td>objective_4</td>\n",
|
| 108 |
-
" <td>0.2</td>\n",
|
| 109 |
-
" <td>0.3</td>\n",
|
| 110 |
-
" </tr>\n",
|
| 111 |
-
" <tr>\n",
|
| 112 |
-
" <th>4</th>\n",
|
| 113 |
-
" <td>objective_5</td>\n",
|
| 114 |
-
" <td>0.2</td>\n",
|
| 115 |
-
" <td>0.4</td>\n",
|
| 116 |
-
" </tr>\n",
|
| 117 |
-
" <tr>\n",
|
| 118 |
-
" <th>5</th>\n",
|
| 119 |
-
" <td>objective_6</td>\n",
|
| 120 |
-
" <td>0.2</td>\n",
|
| 121 |
-
" <td>0.5</td>\n",
|
| 122 |
-
" </tr>\n",
|
| 123 |
-
" <tr>\n",
|
| 124 |
-
" <th>6</th>\n",
|
| 125 |
-
" <td>objective_7</td>\n",
|
| 126 |
-
" <td>0.4</td>\n",
|
| 127 |
-
" <td>0.0</td>\n",
|
| 128 |
-
" </tr>\n",
|
| 129 |
-
" <tr>\n",
|
| 130 |
-
" <th>7</th>\n",
|
| 131 |
-
" <td>objective_8</td>\n",
|
| 132 |
-
" <td>0.4</td>\n",
|
| 133 |
-
" <td>0.1</td>\n",
|
| 134 |
-
" </tr>\n",
|
| 135 |
-
" <tr>\n",
|
| 136 |
-
" <th>8</th>\n",
|
| 137 |
-
" <td>objective_9</td>\n",
|
| 138 |
-
" <td>0.4</td>\n",
|
| 139 |
-
" <td>0.2</td>\n",
|
| 140 |
-
" </tr>\n",
|
| 141 |
-
" <tr>\n",
|
| 142 |
-
" <th>9</th>\n",
|
| 143 |
-
" <td>objective_10</td>\n",
|
| 144 |
-
" <td>0.4</td>\n",
|
| 145 |
-
" <td>0.3</td>\n",
|
| 146 |
-
" </tr>\n",
|
| 147 |
-
" <tr>\n",
|
| 148 |
-
" <th>10</th>\n",
|
| 149 |
-
" <td>objective_11</td>\n",
|
| 150 |
-
" <td>0.4</td>\n",
|
| 151 |
-
" <td>0.4</td>\n",
|
| 152 |
-
" </tr>\n",
|
| 153 |
-
" <tr>\n",
|
| 154 |
-
" <th>11</th>\n",
|
| 155 |
-
" <td>objective_12</td>\n",
|
| 156 |
-
" <td>0.4</td>\n",
|
| 157 |
-
" <td>0.5</td>\n",
|
| 158 |
-
" </tr>\n",
|
| 159 |
-
" <tr>\n",
|
| 160 |
-
" <th>12</th>\n",
|
| 161 |
-
" <td>objective_13</td>\n",
|
| 162 |
-
" <td>0.6</td>\n",
|
| 163 |
-
" <td>0.0</td>\n",
|
| 164 |
-
" </tr>\n",
|
| 165 |
-
" <tr>\n",
|
| 166 |
-
" <th>13</th>\n",
|
| 167 |
-
" <td>objective_14</td>\n",
|
| 168 |
-
" <td>0.6</td>\n",
|
| 169 |
-
" <td>0.1</td>\n",
|
| 170 |
-
" </tr>\n",
|
| 171 |
-
" <tr>\n",
|
| 172 |
-
" <th>14</th>\n",
|
| 173 |
-
" <td>objective_15</td>\n",
|
| 174 |
-
" <td>0.6</td>\n",
|
| 175 |
-
" <td>0.2</td>\n",
|
| 176 |
-
" </tr>\n",
|
| 177 |
-
" <tr>\n",
|
| 178 |
-
" <th>15</th>\n",
|
| 179 |
-
" <td>objective_16</td>\n",
|
| 180 |
-
" <td>0.6</td>\n",
|
| 181 |
-
" <td>0.3</td>\n",
|
| 182 |
-
" </tr>\n",
|
| 183 |
-
" <tr>\n",
|
| 184 |
-
" <th>16</th>\n",
|
| 185 |
-
" <td>objective_17</td>\n",
|
| 186 |
-
" <td>0.6</td>\n",
|
| 187 |
-
" <td>0.4</td>\n",
|
| 188 |
-
" </tr>\n",
|
| 189 |
-
" <tr>\n",
|
| 190 |
-
" <th>17</th>\n",
|
| 191 |
-
" <td>objective_18</td>\n",
|
| 192 |
-
" <td>0.6</td>\n",
|
| 193 |
-
" <td>0.5</td>\n",
|
| 194 |
-
" </tr>\n",
|
| 195 |
-
" <tr>\n",
|
| 196 |
-
" <th>18</th>\n",
|
| 197 |
-
" <td>objective_19</td>\n",
|
| 198 |
-
" <td>0.8</td>\n",
|
| 199 |
-
" <td>0.0</td>\n",
|
| 200 |
-
" </tr>\n",
|
| 201 |
-
" <tr>\n",
|
| 202 |
-
" <th>19</th>\n",
|
| 203 |
-
" <td>objective_20</td>\n",
|
| 204 |
-
" <td>0.8</td>\n",
|
| 205 |
-
" <td>0.1</td>\n",
|
| 206 |
-
" </tr>\n",
|
| 207 |
-
" <tr>\n",
|
| 208 |
-
" <th>20</th>\n",
|
| 209 |
-
" <td>objective_21</td>\n",
|
| 210 |
-
" <td>0.8</td>\n",
|
| 211 |
-
" <td>0.2</td>\n",
|
| 212 |
-
" </tr>\n",
|
| 213 |
-
" <tr>\n",
|
| 214 |
-
" <th>21</th>\n",
|
| 215 |
-
" <td>objective_22</td>\n",
|
| 216 |
-
" <td>0.8</td>\n",
|
| 217 |
-
" <td>0.3</td>\n",
|
| 218 |
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" </tr>\n",
|
| 219 |
-
" <tr>\n",
|
| 220 |
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" <th>22</th>\n",
|
| 221 |
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" <td>objective_23</td>\n",
|
| 222 |
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" <td>0.8</td>\n",
|
| 223 |
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" <td>0.4</td>\n",
|
| 224 |
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" </tr>\n",
|
| 225 |
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" <tr>\n",
|
| 226 |
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" <th>23</th>\n",
|
| 227 |
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" <td>objective_24</td>\n",
|
| 228 |
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" <td>0.8</td>\n",
|
| 229 |
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" <td>0.5</td>\n",
|
| 230 |
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" </tr>\n",
|
| 231 |
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" <tr>\n",
|
| 232 |
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" <th>24</th>\n",
|
| 233 |
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" <td>objective_25</td>\n",
|
| 234 |
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" <td>1.0</td>\n",
|
| 235 |
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" <td>0.0</td>\n",
|
| 236 |
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" </tr>\n",
|
| 237 |
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" <tr>\n",
|
| 238 |
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" <th>25</th>\n",
|
| 239 |
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" <td>objective_26</td>\n",
|
| 240 |
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" <td>1.0</td>\n",
|
| 241 |
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" <td>0.1</td>\n",
|
| 242 |
-
" </tr>\n",
|
| 243 |
-
" <tr>\n",
|
| 244 |
-
" <th>26</th>\n",
|
| 245 |
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" <td>objective_27</td>\n",
|
| 246 |
-
" <td>1.0</td>\n",
|
| 247 |
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" <td>0.2</td>\n",
|
| 248 |
-
" </tr>\n",
|
| 249 |
-
" <tr>\n",
|
| 250 |
-
" <th>27</th>\n",
|
| 251 |
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" <td>objective_28</td>\n",
|
| 252 |
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" <td>1.0</td>\n",
|
| 253 |
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" <td>0.3</td>\n",
|
| 254 |
-
" </tr>\n",
|
| 255 |
-
" <tr>\n",
|
| 256 |
-
" <th>28</th>\n",
|
| 257 |
-
" <td>objective_29</td>\n",
|
| 258 |
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" <td>1.0</td>\n",
|
| 259 |
-
" <td>0.4</td>\n",
|
| 260 |
-
" </tr>\n",
|
| 261 |
-
" <tr>\n",
|
| 262 |
-
" <th>29</th>\n",
|
| 263 |
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" <td>objective_30</td>\n",
|
| 264 |
-
" <td>1.0</td>\n",
|
| 265 |
-
" <td>0.5</td>\n",
|
| 266 |
-
" </tr>\n",
|
| 267 |
-
" </tbody>\n",
|
| 268 |
-
"</table>\n",
|
| 269 |
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"</div>"
|
| 270 |
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],
|
| 271 |
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"text/plain": [
|
| 272 |
-
" log ratio_top_20_variants \n",
|
| 273 |
-
"0 objective_1 0.2 \\\n",
|
| 274 |
-
"1 objective_2 0.2 \n",
|
| 275 |
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"2 objective_3 0.2 \n",
|
| 276 |
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"3 objective_4 0.2 \n",
|
| 277 |
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"4 objective_5 0.2 \n",
|
| 278 |
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"5 objective_6 0.2 \n",
|
| 279 |
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"6 objective_7 0.4 \n",
|
| 280 |
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"7 objective_8 0.4 \n",
|
| 281 |
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"8 objective_9 0.4 \n",
|
| 282 |
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"9 objective_10 0.4 \n",
|
| 283 |
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"10 objective_11 0.4 \n",
|
| 284 |
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"11 objective_12 0.4 \n",
|
| 285 |
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"12 objective_13 0.6 \n",
|
| 286 |
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"13 objective_14 0.6 \n",
|
| 287 |
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"14 objective_15 0.6 \n",
|
| 288 |
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"15 objective_16 0.6 \n",
|
| 289 |
-
"16 objective_17 0.6 \n",
|
| 290 |
-
"17 objective_18 0.6 \n",
|
| 291 |
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"18 objective_19 0.8 \n",
|
| 292 |
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"19 objective_20 0.8 \n",
|
| 293 |
-
"20 objective_21 0.8 \n",
|
| 294 |
-
"21 objective_22 0.8 \n",
|
| 295 |
-
"22 objective_23 0.8 \n",
|
| 296 |
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"23 objective_24 0.8 \n",
|
| 297 |
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"24 objective_25 1.0 \n",
|
| 298 |
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"25 objective_26 1.0 \n",
|
| 299 |
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"26 objective_27 1.0 \n",
|
| 300 |
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"27 objective_28 1.0 \n",
|
| 301 |
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"28 objective_29 1.0 \n",
|
| 302 |
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"29 objective_30 1.0 \n",
|
| 303 |
-
"\n",
|
| 304 |
-
" normalized_sequence_entropy_linear_forgetting \n",
|
| 305 |
-
"0 0.0 \n",
|
| 306 |
-
"1 0.1 \n",
|
| 307 |
-
"2 0.2 \n",
|
| 308 |
-
"3 0.3 \n",
|
| 309 |
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"4 0.4 \n",
|
| 310 |
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"5 0.5 \n",
|
| 311 |
-
"6 0.0 \n",
|
| 312 |
-
"7 0.1 \n",
|
| 313 |
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"8 0.2 \n",
|
| 314 |
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"9 0.3 \n",
|
| 315 |
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"10 0.4 \n",
|
| 316 |
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"11 0.5 \n",
|
| 317 |
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"12 0.0 \n",
|
| 318 |
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"13 0.1 \n",
|
| 319 |
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"14 0.2 \n",
|
| 320 |
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"15 0.3 \n",
|
| 321 |
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"16 0.4 \n",
|
| 322 |
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"17 0.5 \n",
|
| 323 |
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"18 0.0 \n",
|
| 324 |
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"19 0.1 \n",
|
| 325 |
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"20 0.2 \n",
|
| 326 |
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"21 0.3 \n",
|
| 327 |
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"22 0.4 \n",
|
| 328 |
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"23 0.5 \n",
|
| 329 |
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"24 0.0 \n",
|
| 330 |
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"25 0.1 \n",
|
| 331 |
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"26 0.2 \n",
|
| 332 |
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"27 0.3 \n",
|
| 333 |
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"28 0.4 \n",
|
| 334 |
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"29 0.5 "
|
| 335 |
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]
|
| 336 |
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},
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| 337 |
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"execution_count": 32,
|
| 338 |
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"metadata": {},
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| 339 |
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"output_type": "execute_result"
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| 340 |
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}
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],
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| 342 |
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"source": [
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| 343 |
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"df"
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| 344 |
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d726a5ae",
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"metadata": {},
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notebooks/.ipynb_checkpoints/oversampling-checkpoint.ipynb
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notebooks/.ipynb_checkpoints/performance_feature_correlation-checkpoint.ipynb
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notebooks/.ipynb_checkpoints/statistics_tasks_to_datasets-checkpoint.ipynb
DELETED
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"execution_count": 4,
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"id": "4827785f",
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"metadata": {},
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| 28 |
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" <tr style=\"text-align: right;\">\n",
|
| 29 |
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" <th></th>\n",
|
| 30 |
-
" <th>Name</th>\n",
|
| 31 |
-
" <th>Short description</th>\n",
|
| 32 |
-
" <th>data link</th>\n",
|
| 33 |
-
" <th>challenge link</th>\n",
|
| 34 |
-
" <th>Citations (Stand Februar 2023)</th>\n",
|
| 35 |
-
" <th>Publications</th>\n",
|
| 36 |
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" <th>Process Discovery/ Declarative</th>\n",
|
| 37 |
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" <th>Conformance Checking / Alignment / Replay</th>\n",
|
| 38 |
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" <th>Online / Streaming / Realtime</th>\n",
|
| 39 |
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" <th>Performance (Analysis) / Temporal / Time</th>\n",
|
| 40 |
-
" <th>Predict(ive)/ Monitoring/ Prescriptive</th>\n",
|
| 41 |
-
" <th>Trace clustering / Clustering</th>\n",
|
| 42 |
-
" <th>Preprocessing / Event Abstraction / Event Data Correlation</th>\n",
|
| 43 |
-
" <th>Further keywords:</th>\n",
|
| 44 |
-
" </tr>\n",
|
| 45 |
-
" </thead>\n",
|
| 46 |
-
" <tbody>\n",
|
| 47 |
-
" <tr>\n",
|
| 48 |
-
" <th>0</th>\n",
|
| 49 |
-
" <td>Sepsis Cases - Event Log</td>\n",
|
| 50 |
-
" <td>This real-life event log contains events of se...</td>\n",
|
| 51 |
-
" <td>https://data.4tu.nl/articles/dataset/Sepsis_Ca...</td>\n",
|
| 52 |
-
" <td>https://data.4tu.nl/articles/dataset/Sepsis_Ca...</td>\n",
|
| 53 |
-
" <td>61</td>\n",
|
| 54 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 55 |
-
" <td>17</td>\n",
|
| 56 |
-
" <td>7</td>\n",
|
| 57 |
-
" <td>4</td>\n",
|
| 58 |
-
" <td>1</td>\n",
|
| 59 |
-
" <td>8</td>\n",
|
| 60 |
-
" <td>2</td>\n",
|
| 61 |
-
" <td>2</td>\n",
|
| 62 |
-
" <td>(machine) learning, (online process) monitorin...</td>\n",
|
| 63 |
-
" </tr>\n",
|
| 64 |
-
" <tr>\n",
|
| 65 |
-
" <th>1</th>\n",
|
| 66 |
-
" <td>BPI 2017 - Offer Log</td>\n",
|
| 67 |
-
" <td>Contains data from a financial institute inclu...</td>\n",
|
| 68 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 69 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2017:ch...</td>\n",
|
| 70 |
-
" <td>4</td>\n",
|
| 71 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 72 |
-
" <td>1</td>\n",
|
| 73 |
-
" <td>0</td>\n",
|
| 74 |
-
" <td>0</td>\n",
|
| 75 |
-
" <td>1</td>\n",
|
| 76 |
-
" <td>1</td>\n",
|
| 77 |
-
" <td>0</td>\n",
|
| 78 |
-
" <td>0</td>\n",
|
| 79 |
-
" <td>(machine) learning, cloud computing</td>\n",
|
| 80 |
-
" </tr>\n",
|
| 81 |
-
" <tr>\n",
|
| 82 |
-
" <th>2</th>\n",
|
| 83 |
-
" <td>Road Traffic Fine Management Process (not BPI)</td>\n",
|
| 84 |
-
" <td>A real-life event log taken from an informatio...</td>\n",
|
| 85 |
-
" <td>https://data.4tu.nl/articles/dataset/Road_Traf...</td>\n",
|
| 86 |
-
" <td>NaN</td>\n",
|
| 87 |
-
" <td>95</td>\n",
|
| 88 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 89 |
-
" <td>32</td>\n",
|
| 90 |
-
" <td>9</td>\n",
|
| 91 |
-
" <td>4</td>\n",
|
| 92 |
-
" <td>8</td>\n",
|
| 93 |
-
" <td>15</td>\n",
|
| 94 |
-
" <td>1</td>\n",
|
| 95 |
-
" <td>2</td>\n",
|
| 96 |
-
" <td>alarm-based prescriptive process monitoring, b...</td>\n",
|
| 97 |
-
" </tr>\n",
|
| 98 |
-
" <tr>\n",
|
| 99 |
-
" <th>3</th>\n",
|
| 100 |
-
" <td>BPI 2011</td>\n",
|
| 101 |
-
" <td>Contains data from from a Dutch Academic Hospi...</td>\n",
|
| 102 |
-
" <td>https://data.4tu.nl/articles/dataset/Real-life...</td>\n",
|
| 103 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2011:ch...</td>\n",
|
| 104 |
-
" <td>57</td>\n",
|
| 105 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 106 |
-
" <td>13</td>\n",
|
| 107 |
-
" <td>1</td>\n",
|
| 108 |
-
" <td>3</td>\n",
|
| 109 |
-
" <td>4</td>\n",
|
| 110 |
-
" <td>12</td>\n",
|
| 111 |
-
" <td>4</td>\n",
|
| 112 |
-
" <td>1</td>\n",
|
| 113 |
-
" <td>(compliance) monitoring, (machine) learning, d...</td>\n",
|
| 114 |
-
" </tr>\n",
|
| 115 |
-
" <tr>\n",
|
| 116 |
-
" <th>4</th>\n",
|
| 117 |
-
" <td>BPI 2012</td>\n",
|
| 118 |
-
" <td>Contains the event log of an application proce...</td>\n",
|
| 119 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 120 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2012:ch...</td>\n",
|
| 121 |
-
" <td>151</td>\n",
|
| 122 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 123 |
-
" <td>40</td>\n",
|
| 124 |
-
" <td>15</td>\n",
|
| 125 |
-
" <td>4</td>\n",
|
| 126 |
-
" <td>13</td>\n",
|
| 127 |
-
" <td>46</td>\n",
|
| 128 |
-
" <td>0</td>\n",
|
| 129 |
-
" <td>1</td>\n",
|
| 130 |
-
" <td>(in)frequent patterns in process models, (mach...</td>\n",
|
| 131 |
-
" </tr>\n",
|
| 132 |
-
" <tr>\n",
|
| 133 |
-
" <th>5</th>\n",
|
| 134 |
-
" <td>BPI 2013 - Open Problems</td>\n",
|
| 135 |
-
" <td>Rabobank Group ICT implemented ITIL processes ...</td>\n",
|
| 136 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 137 |
-
" <td>https://www.win.tue.nl/bpi/2013/challenge.html</td>\n",
|
| 138 |
-
" <td>6</td>\n",
|
| 139 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 140 |
-
" <td>1</td>\n",
|
| 141 |
-
" <td>0</td>\n",
|
| 142 |
-
" <td>0</td>\n",
|
| 143 |
-
" <td>0</td>\n",
|
| 144 |
-
" <td>1</td>\n",
|
| 145 |
-
" <td>0</td>\n",
|
| 146 |
-
" <td>0</td>\n",
|
| 147 |
-
" <td>(in)frequent patterns in process models, (mach...</td>\n",
|
| 148 |
-
" </tr>\n",
|
| 149 |
-
" <tr>\n",
|
| 150 |
-
" <th>6</th>\n",
|
| 151 |
-
" <td>BPI 2013 - Closed Problems</td>\n",
|
| 152 |
-
" <td>Rabobank Group ICT implemented ITIL processes ...</td>\n",
|
| 153 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 154 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2013:ch...</td>\n",
|
| 155 |
-
" <td>12</td>\n",
|
| 156 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 157 |
-
" <td>3</td>\n",
|
| 158 |
-
" <td>2</td>\n",
|
| 159 |
-
" <td>1</td>\n",
|
| 160 |
-
" <td>2</td>\n",
|
| 161 |
-
" <td>0</td>\n",
|
| 162 |
-
" <td>0</td>\n",
|
| 163 |
-
" <td>3</td>\n",
|
| 164 |
-
" <td>(in)frequent patterns in process models</td>\n",
|
| 165 |
-
" </tr>\n",
|
| 166 |
-
" <tr>\n",
|
| 167 |
-
" <th>7</th>\n",
|
| 168 |
-
" <td>BPI 2013 - Incidents</td>\n",
|
| 169 |
-
" <td>The log contains events from an incident and p...</td>\n",
|
| 170 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 171 |
-
" <td>https://www.win.tue.nl/bpi/2013/challenge.html</td>\n",
|
| 172 |
-
" <td>36</td>\n",
|
| 173 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 174 |
-
" <td>14</td>\n",
|
| 175 |
-
" <td>5</td>\n",
|
| 176 |
-
" <td>1</td>\n",
|
| 177 |
-
" <td>1</td>\n",
|
| 178 |
-
" <td>7</td>\n",
|
| 179 |
-
" <td>0</td>\n",
|
| 180 |
-
" <td>2</td>\n",
|
| 181 |
-
" <td>(machine) learning, rule mining</td>\n",
|
| 182 |
-
" </tr>\n",
|
| 183 |
-
" <tr>\n",
|
| 184 |
-
" <th>8</th>\n",
|
| 185 |
-
" <td>BPI 2014 - Incident Records</td>\n",
|
| 186 |
-
" <td>Rabobank Group ICT implemented ITIL processes ...</td>\n",
|
| 187 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 188 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2014:ch...</td>\n",
|
| 189 |
-
" <td>5</td>\n",
|
| 190 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 191 |
-
" <td>1</td>\n",
|
| 192 |
-
" <td>0</td>\n",
|
| 193 |
-
" <td>0</td>\n",
|
| 194 |
-
" <td>0</td>\n",
|
| 195 |
-
" <td>0</td>\n",
|
| 196 |
-
" <td>0</td>\n",
|
| 197 |
-
" <td>0</td>\n",
|
| 198 |
-
" <td>privacy preservation, security</td>\n",
|
| 199 |
-
" </tr>\n",
|
| 200 |
-
" <tr>\n",
|
| 201 |
-
" <th>9</th>\n",
|
| 202 |
-
" <td>BPI 2014 - Interaction Records</td>\n",
|
| 203 |
-
" <td>Rabobank Group ICT implemented ITIL processes ...</td>\n",
|
| 204 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 205 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2014:ch...</td>\n",
|
| 206 |
-
" <td>1</td>\n",
|
| 207 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 208 |
-
" <td>0</td>\n",
|
| 209 |
-
" <td>0</td>\n",
|
| 210 |
-
" <td>0</td>\n",
|
| 211 |
-
" <td>0</td>\n",
|
| 212 |
-
" <td>0</td>\n",
|
| 213 |
-
" <td>0</td>\n",
|
| 214 |
-
" <td>0</td>\n",
|
| 215 |
-
" <td>(machine) learning, hidden Markov models</td>\n",
|
| 216 |
-
" </tr>\n",
|
| 217 |
-
" <tr>\n",
|
| 218 |
-
" <th>10</th>\n",
|
| 219 |
-
" <td>BPI 2015 - Log 3</td>\n",
|
| 220 |
-
" <td>Provided by 5 Dutch municipalities. The data c...</td>\n",
|
| 221 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 222 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2015:ch...</td>\n",
|
| 223 |
-
" <td>1</td>\n",
|
| 224 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 225 |
-
" <td>0</td>\n",
|
| 226 |
-
" <td>0</td>\n",
|
| 227 |
-
" <td>0</td>\n",
|
| 228 |
-
" <td>0</td>\n",
|
| 229 |
-
" <td>1</td>\n",
|
| 230 |
-
" <td>0</td>\n",
|
| 231 |
-
" <td>0</td>\n",
|
| 232 |
-
" <td>specification-driven predictive business proce...</td>\n",
|
| 233 |
-
" </tr>\n",
|
| 234 |
-
" <tr>\n",
|
| 235 |
-
" <th>11</th>\n",
|
| 236 |
-
" <td>BPI 2015 - Log 1</td>\n",
|
| 237 |
-
" <td>Provided by 5 Dutch municipalities. The data c...</td>\n",
|
| 238 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 239 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2015:ch...</td>\n",
|
| 240 |
-
" <td>8</td>\n",
|
| 241 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 242 |
-
" <td>1</td>\n",
|
| 243 |
-
" <td>1</td>\n",
|
| 244 |
-
" <td>0</td>\n",
|
| 245 |
-
" <td>0</td>\n",
|
| 246 |
-
" <td>3</td>\n",
|
| 247 |
-
" <td>0</td>\n",
|
| 248 |
-
" <td>3</td>\n",
|
| 249 |
-
" <td>(machine) learning</td>\n",
|
| 250 |
-
" </tr>\n",
|
| 251 |
-
" <tr>\n",
|
| 252 |
-
" <th>12</th>\n",
|
| 253 |
-
" <td>BPI 2016 - Clicks Logged In</td>\n",
|
| 254 |
-
" <td>Contains clicks of users that are logged in fr...</td>\n",
|
| 255 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 256 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2016:ch...</td>\n",
|
| 257 |
-
" <td>1</td>\n",
|
| 258 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 259 |
-
" <td>1</td>\n",
|
| 260 |
-
" <td>0</td>\n",
|
| 261 |
-
" <td>1</td>\n",
|
| 262 |
-
" <td>0</td>\n",
|
| 263 |
-
" <td>0</td>\n",
|
| 264 |
-
" <td>0</td>\n",
|
| 265 |
-
" <td>0</td>\n",
|
| 266 |
-
" <td>automation</td>\n",
|
| 267 |
-
" </tr>\n",
|
| 268 |
-
" <tr>\n",
|
| 269 |
-
" <th>13</th>\n",
|
| 270 |
-
" <td>BPI 2017 - Application Log</td>\n",
|
| 271 |
-
" <td>Contains data from a financial institute inclu...</td>\n",
|
| 272 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 273 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2017:ch...</td>\n",
|
| 274 |
-
" <td>73</td>\n",
|
| 275 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 276 |
-
" <td>11</td>\n",
|
| 277 |
-
" <td>5</td>\n",
|
| 278 |
-
" <td>2</td>\n",
|
| 279 |
-
" <td>14</td>\n",
|
| 280 |
-
" <td>23</td>\n",
|
| 281 |
-
" <td>1</td>\n",
|
| 282 |
-
" <td>1</td>\n",
|
| 283 |
-
" <td>(machine) learning, alarm-based prescriptive p...</td>\n",
|
| 284 |
-
" </tr>\n",
|
| 285 |
-
" <tr>\n",
|
| 286 |
-
" <th>14</th>\n",
|
| 287 |
-
" <td>BPI 2018</td>\n",
|
| 288 |
-
" <td>The process covers the handling of application...</td>\n",
|
| 289 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 290 |
-
" <td>https://www.win.tue.nl/bpi/doku.php?id=2018:ch...</td>\n",
|
| 291 |
-
" <td>26</td>\n",
|
| 292 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 293 |
-
" <td>7</td>\n",
|
| 294 |
-
" <td>1</td>\n",
|
| 295 |
-
" <td>2</td>\n",
|
| 296 |
-
" <td>0</td>\n",
|
| 297 |
-
" <td>8</td>\n",
|
| 298 |
-
" <td>0</td>\n",
|
| 299 |
-
" <td>2</td>\n",
|
| 300 |
-
" <td>(machine) learning, automation</td>\n",
|
| 301 |
-
" </tr>\n",
|
| 302 |
-
" <tr>\n",
|
| 303 |
-
" <th>15</th>\n",
|
| 304 |
-
" <td>BPI 2020 - Travel Permits</td>\n",
|
| 305 |
-
" <td>Contains 2 years of data from the reimbursemen...</td>\n",
|
| 306 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 307 |
-
" <td>https://icpmconference.org/2020/bpi-challenge/</td>\n",
|
| 308 |
-
" <td>2</td>\n",
|
| 309 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 310 |
-
" <td>0</td>\n",
|
| 311 |
-
" <td>0</td>\n",
|
| 312 |
-
" <td>0</td>\n",
|
| 313 |
-
" <td>1</td>\n",
|
| 314 |
-
" <td>0</td>\n",
|
| 315 |
-
" <td>0</td>\n",
|
| 316 |
-
" <td>0</td>\n",
|
| 317 |
-
" <td>stage-based process performance analysis</td>\n",
|
| 318 |
-
" </tr>\n",
|
| 319 |
-
" <tr>\n",
|
| 320 |
-
" <th>16</th>\n",
|
| 321 |
-
" <td>BPI 2019</td>\n",
|
| 322 |
-
" <td>Contains the purchase order handling process o...</td>\n",
|
| 323 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 324 |
-
" <td>https://icpmconference.org/2019/icpm-2019/cont...</td>\n",
|
| 325 |
-
" <td>35</td>\n",
|
| 326 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 327 |
-
" <td>3</td>\n",
|
| 328 |
-
" <td>1</td>\n",
|
| 329 |
-
" <td>6</td>\n",
|
| 330 |
-
" <td>6</td>\n",
|
| 331 |
-
" <td>9</td>\n",
|
| 332 |
-
" <td>4</td>\n",
|
| 333 |
-
" <td>1</td>\n",
|
| 334 |
-
" <td>(online process) monitoring, remaining time pr...</td>\n",
|
| 335 |
-
" </tr>\n",
|
| 336 |
-
" <tr>\n",
|
| 337 |
-
" <th>17</th>\n",
|
| 338 |
-
" <td>BPI 2020 - International Declarations</td>\n",
|
| 339 |
-
" <td>Contains 2 years of data from the reimbursemen...</td>\n",
|
| 340 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 341 |
-
" <td>https://icpmconference.org/2020/bpi-challenge/</td>\n",
|
| 342 |
-
" <td>2</td>\n",
|
| 343 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 344 |
-
" <td>0</td>\n",
|
| 345 |
-
" <td>0</td>\n",
|
| 346 |
-
" <td>0</td>\n",
|
| 347 |
-
" <td>1</td>\n",
|
| 348 |
-
" <td>2</td>\n",
|
| 349 |
-
" <td>0</td>\n",
|
| 350 |
-
" <td>0</td>\n",
|
| 351 |
-
" <td>(machine) learning, remaining time prediction</td>\n",
|
| 352 |
-
" </tr>\n",
|
| 353 |
-
" <tr>\n",
|
| 354 |
-
" <th>18</th>\n",
|
| 355 |
-
" <td>BPI 2020 - Domestic Declarations</td>\n",
|
| 356 |
-
" <td>Contains 2 years of data from the reimbursemen...</td>\n",
|
| 357 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 358 |
-
" <td>https://icpmconference.org/2020/bpi-challenge/</td>\n",
|
| 359 |
-
" <td>7</td>\n",
|
| 360 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 361 |
-
" <td>0</td>\n",
|
| 362 |
-
" <td>2</td>\n",
|
| 363 |
-
" <td>2</td>\n",
|
| 364 |
-
" <td>2</td>\n",
|
| 365 |
-
" <td>3</td>\n",
|
| 366 |
-
" <td>0</td>\n",
|
| 367 |
-
" <td>0</td>\n",
|
| 368 |
-
" <td>(machine) learning, remaining time prediction</td>\n",
|
| 369 |
-
" </tr>\n",
|
| 370 |
-
" <tr>\n",
|
| 371 |
-
" <th>19</th>\n",
|
| 372 |
-
" <td>BPI 2020 - Prepaid Travel Cost</td>\n",
|
| 373 |
-
" <td>Contains 2 years of data from the reimbursemen...</td>\n",
|
| 374 |
-
" <td>https://data.4tu.nl/articles/dataset/BPI_Chall...</td>\n",
|
| 375 |
-
" <td>https://icpmconference.org/2020/bpi-challenge/</td>\n",
|
| 376 |
-
" <td>2</td>\n",
|
| 377 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 378 |
-
" <td>0</td>\n",
|
| 379 |
-
" <td>0</td>\n",
|
| 380 |
-
" <td>0</td>\n",
|
| 381 |
-
" <td>0</td>\n",
|
| 382 |
-
" <td>0</td>\n",
|
| 383 |
-
" <td>0</td>\n",
|
| 384 |
-
" <td>0</td>\n",
|
| 385 |
-
" <td>multi-perspective</td>\n",
|
| 386 |
-
" </tr>\n",
|
| 387 |
-
" <tr>\n",
|
| 388 |
-
" <th>20</th>\n",
|
| 389 |
-
" <td>Helpdesk</td>\n",
|
| 390 |
-
" <td>Ticketing management process of the Help desk ...</td>\n",
|
| 391 |
-
" <td>https://data.4tu.nl/articles/dataset/Dataset_b...</td>\n",
|
| 392 |
-
" <td>NaN</td>\n",
|
| 393 |
-
" <td>20</td>\n",
|
| 394 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 395 |
-
" <td>4</td>\n",
|
| 396 |
-
" <td>1</td>\n",
|
| 397 |
-
" <td>3</td>\n",
|
| 398 |
-
" <td>1</td>\n",
|
| 399 |
-
" <td>8</td>\n",
|
| 400 |
-
" <td>0</td>\n",
|
| 401 |
-
" <td>0</td>\n",
|
| 402 |
-
" <td>(machine) learning, drift detection</td>\n",
|
| 403 |
-
" </tr>\n",
|
| 404 |
-
" <tr>\n",
|
| 405 |
-
" <th>21</th>\n",
|
| 406 |
-
" <td>Receipt phase of an environmental permit appli...</td>\n",
|
| 407 |
-
" <td>Data originates from the CoSeLoG project where...</td>\n",
|
| 408 |
-
" <td>https://data.4tu.nl/articles/dataset/Receipt_p...</td>\n",
|
| 409 |
-
" <td>NaN</td>\n",
|
| 410 |
-
" <td>15</td>\n",
|
| 411 |
-
" <td>https://data.4tu.nl/articles/dataset/Receipt_p...</td>\n",
|
| 412 |
-
" <td>-1</td>\n",
|
| 413 |
-
" <td>-1</td>\n",
|
| 414 |
-
" <td>-1</td>\n",
|
| 415 |
-
" <td>-1</td>\n",
|
| 416 |
-
" <td>-1</td>\n",
|
| 417 |
-
" <td>-1</td>\n",
|
| 418 |
-
" <td>-1</td>\n",
|
| 419 |
-
" <td>NaN</td>\n",
|
| 420 |
-
" </tr>\n",
|
| 421 |
-
" <tr>\n",
|
| 422 |
-
" <th>22</th>\n",
|
| 423 |
-
" <td>Environmental permit application process (‘WAB...</td>\n",
|
| 424 |
-
" <td>Data originates from the CoSeLoG project where...</td>\n",
|
| 425 |
-
" <td>https://data.4tu.nl/articles/dataset/Environme...</td>\n",
|
| 426 |
-
" <td>NaN</td>\n",
|
| 427 |
-
" <td>2</td>\n",
|
| 428 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 429 |
-
" <td>0</td>\n",
|
| 430 |
-
" <td>0</td>\n",
|
| 431 |
-
" <td>0</td>\n",
|
| 432 |
-
" <td>0</td>\n",
|
| 433 |
-
" <td>1</td>\n",
|
| 434 |
-
" <td>0</td>\n",
|
| 435 |
-
" <td>0</td>\n",
|
| 436 |
-
" <td>predictions with a-priori knowledge</td>\n",
|
| 437 |
-
" </tr>\n",
|
| 438 |
-
" <tr>\n",
|
| 439 |
-
" <th>23</th>\n",
|
| 440 |
-
" <td>Environmental permit application process (‘WAB...</td>\n",
|
| 441 |
-
" <td>Data originates from the CoSeLoG project where...</td>\n",
|
| 442 |
-
" <td>https://data.4tu.nl/articles/dataset/Environme...</td>\n",
|
| 443 |
-
" <td>NaN</td>\n",
|
| 444 |
-
" <td>2</td>\n",
|
| 445 |
-
" <td>https://app.dimensions.ai/discover/publication...</td>\n",
|
| 446 |
-
" <td>1</td>\n",
|
| 447 |
-
" <td>0</td>\n",
|
| 448 |
-
" <td>0</td>\n",
|
| 449 |
-
" <td>0</td>\n",
|
| 450 |
-
" <td>0</td>\n",
|
| 451 |
-
" <td>0</td>\n",
|
| 452 |
-
" <td>0</td>\n",
|
| 453 |
-
" <td>multidimensional process mining, process cubes</td>\n",
|
| 454 |
-
" </tr>\n",
|
| 455 |
-
" <tr>\n",
|
| 456 |
-
" <th>24</th>\n",
|
| 457 |
-
" <td>NaN</td>\n",
|
| 458 |
-
" <td>NaN</td>\n",
|
| 459 |
-
" <td>NaN</td>\n",
|
| 460 |
-
" <td>NaN</td>\n",
|
| 461 |
-
" <td>NaN</td>\n",
|
| 462 |
-
" <td>NaN</td>\n",
|
| 463 |
-
" <td>NaN</td>\n",
|
| 464 |
-
" <td>NaN</td>\n",
|
| 465 |
-
" <td>NaN</td>\n",
|
| 466 |
-
" <td>NaN</td>\n",
|
| 467 |
-
" <td>NaN</td>\n",
|
| 468 |
-
" <td>NaN</td>\n",
|
| 469 |
-
" <td>NaN</td>\n",
|
| 470 |
-
" <td>NaN</td>\n",
|
| 471 |
-
" </tr>\n",
|
| 472 |
-
" </tbody>\n",
|
| 473 |
-
"</table>\n",
|
| 474 |
-
"</div>"
|
| 475 |
-
],
|
| 476 |
-
"text/plain": [
|
| 477 |
-
" Name \\\n",
|
| 478 |
-
"0 Sepsis Cases - Event Log \n",
|
| 479 |
-
"1 BPI 2017 - Offer Log \n",
|
| 480 |
-
"2 Road Traffic Fine Management Process (not BPI) \n",
|
| 481 |
-
"3 BPI 2011 \n",
|
| 482 |
-
"4 BPI 2012 \n",
|
| 483 |
-
"5 BPI 2013 - Open Problems \n",
|
| 484 |
-
"6 BPI 2013 - Closed Problems \n",
|
| 485 |
-
"7 BPI 2013 - Incidents \n",
|
| 486 |
-
"8 BPI 2014 - Incident Records \n",
|
| 487 |
-
"9 BPI 2014 - Interaction Records \n",
|
| 488 |
-
"10 BPI 2015 - Log 3 \n",
|
| 489 |
-
"11 BPI 2015 - Log 1 \n",
|
| 490 |
-
"12 BPI 2016 - Clicks Logged In \n",
|
| 491 |
-
"13 BPI 2017 - Application Log \n",
|
| 492 |
-
"14 BPI 2018 \n",
|
| 493 |
-
"15 BPI 2020 - Travel Permits \n",
|
| 494 |
-
"16 BPI 2019 \n",
|
| 495 |
-
"17 BPI 2020 - International Declarations \n",
|
| 496 |
-
"18 BPI 2020 - Domestic Declarations \n",
|
| 497 |
-
"19 BPI 2020 - Prepaid Travel Cost \n",
|
| 498 |
-
"20 Helpdesk \n",
|
| 499 |
-
"21 Receipt phase of an environmental permit appli... \n",
|
| 500 |
-
"22 Environmental permit application process (‘WAB... \n",
|
| 501 |
-
"23 Environmental permit application process (‘WAB... \n",
|
| 502 |
-
"24 NaN \n",
|
| 503 |
-
"\n",
|
| 504 |
-
" Short description \\\n",
|
| 505 |
-
"0 This real-life event log contains events of se... \n",
|
| 506 |
-
"1 Contains data from a financial institute inclu... \n",
|
| 507 |
-
"2 A real-life event log taken from an informatio... \n",
|
| 508 |
-
"3 Contains data from from a Dutch Academic Hospi... \n",
|
| 509 |
-
"4 Contains the event log of an application proce... \n",
|
| 510 |
-
"5 Rabobank Group ICT implemented ITIL processes ... \n",
|
| 511 |
-
"6 Rabobank Group ICT implemented ITIL processes ... \n",
|
| 512 |
-
"7 The log contains events from an incident and p... \n",
|
| 513 |
-
"8 Rabobank Group ICT implemented ITIL processes ... \n",
|
| 514 |
-
"9 Rabobank Group ICT implemented ITIL processes ... \n",
|
| 515 |
-
"10 Provided by 5 Dutch municipalities. The data c... \n",
|
| 516 |
-
"11 Provided by 5 Dutch municipalities. The data c... \n",
|
| 517 |
-
"12 Contains clicks of users that are logged in fr... \n",
|
| 518 |
-
"13 Contains data from a financial institute inclu... \n",
|
| 519 |
-
"14 The process covers the handling of application... \n",
|
| 520 |
-
"15 Contains 2 years of data from the reimbursemen... \n",
|
| 521 |
-
"16 Contains the purchase order handling process o... \n",
|
| 522 |
-
"17 Contains 2 years of data from the reimbursemen... \n",
|
| 523 |
-
"18 Contains 2 years of data from the reimbursemen... \n",
|
| 524 |
-
"19 Contains 2 years of data from the reimbursemen... \n",
|
| 525 |
-
"20 Ticketing management process of the Help desk ... \n",
|
| 526 |
-
"21 Data originates from the CoSeLoG project where... \n",
|
| 527 |
-
"22 Data originates from the CoSeLoG project where... \n",
|
| 528 |
-
"23 Data originates from the CoSeLoG project where... \n",
|
| 529 |
-
"24 NaN \n",
|
| 530 |
-
"\n",
|
| 531 |
-
" data link \\\n",
|
| 532 |
-
"0 https://data.4tu.nl/articles/dataset/Sepsis_Ca... \n",
|
| 533 |
-
"1 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 534 |
-
"2 https://data.4tu.nl/articles/dataset/Road_Traf... \n",
|
| 535 |
-
"3 https://data.4tu.nl/articles/dataset/Real-life... \n",
|
| 536 |
-
"4 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 537 |
-
"5 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 538 |
-
"6 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 539 |
-
"7 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 540 |
-
"8 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 541 |
-
"9 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 542 |
-
"10 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 543 |
-
"11 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 544 |
-
"12 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 545 |
-
"13 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 546 |
-
"14 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 547 |
-
"15 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 548 |
-
"16 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 549 |
-
"17 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 550 |
-
"18 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 551 |
-
"19 https://data.4tu.nl/articles/dataset/BPI_Chall... \n",
|
| 552 |
-
"20 https://data.4tu.nl/articles/dataset/Dataset_b... \n",
|
| 553 |
-
"21 https://data.4tu.nl/articles/dataset/Receipt_p... \n",
|
| 554 |
-
"22 https://data.4tu.nl/articles/dataset/Environme... \n",
|
| 555 |
-
"23 https://data.4tu.nl/articles/dataset/Environme... \n",
|
| 556 |
-
"24 NaN \n",
|
| 557 |
-
"\n",
|
| 558 |
-
" challenge link \\\n",
|
| 559 |
-
"0 https://data.4tu.nl/articles/dataset/Sepsis_Ca... \n",
|
| 560 |
-
"1 https://www.win.tue.nl/bpi/doku.php?id=2017:ch... \n",
|
| 561 |
-
"2 NaN \n",
|
| 562 |
-
"3 https://www.win.tue.nl/bpi/doku.php?id=2011:ch... \n",
|
| 563 |
-
"4 https://www.win.tue.nl/bpi/doku.php?id=2012:ch... \n",
|
| 564 |
-
"5 https://www.win.tue.nl/bpi/2013/challenge.html \n",
|
| 565 |
-
"6 https://www.win.tue.nl/bpi/doku.php?id=2013:ch... \n",
|
| 566 |
-
"7 https://www.win.tue.nl/bpi/2013/challenge.html \n",
|
| 567 |
-
"8 https://www.win.tue.nl/bpi/doku.php?id=2014:ch... \n",
|
| 568 |
-
"9 https://www.win.tue.nl/bpi/doku.php?id=2014:ch... \n",
|
| 569 |
-
"10 https://www.win.tue.nl/bpi/doku.php?id=2015:ch... \n",
|
| 570 |
-
"11 https://www.win.tue.nl/bpi/doku.php?id=2015:ch... \n",
|
| 571 |
-
"12 https://www.win.tue.nl/bpi/doku.php?id=2016:ch... \n",
|
| 572 |
-
"13 https://www.win.tue.nl/bpi/doku.php?id=2017:ch... \n",
|
| 573 |
-
"14 https://www.win.tue.nl/bpi/doku.php?id=2018:ch... \n",
|
| 574 |
-
"15 https://icpmconference.org/2020/bpi-challenge/ \n",
|
| 575 |
-
"16 https://icpmconference.org/2019/icpm-2019/cont... \n",
|
| 576 |
-
"17 https://icpmconference.org/2020/bpi-challenge/ \n",
|
| 577 |
-
"18 https://icpmconference.org/2020/bpi-challenge/ \n",
|
| 578 |
-
"19 https://icpmconference.org/2020/bpi-challenge/ \n",
|
| 579 |
-
"20 NaN \n",
|
| 580 |
-
"21 NaN \n",
|
| 581 |
-
"22 NaN \n",
|
| 582 |
-
"23 NaN \n",
|
| 583 |
-
"24 NaN \n",
|
| 584 |
-
"\n",
|
| 585 |
-
" Citations (Stand Februar 2023) \\\n",
|
| 586 |
-
"0 61 \n",
|
| 587 |
-
"1 4 \n",
|
| 588 |
-
"2 95 \n",
|
| 589 |
-
"3 57 \n",
|
| 590 |
-
"4 151 \n",
|
| 591 |
-
"5 6 \n",
|
| 592 |
-
"6 12 \n",
|
| 593 |
-
"7 36 \n",
|
| 594 |
-
"8 5 \n",
|
| 595 |
-
"9 1 \n",
|
| 596 |
-
"10 1 \n",
|
| 597 |
-
"11 8 \n",
|
| 598 |
-
"12 1 \n",
|
| 599 |
-
"13 73 \n",
|
| 600 |
-
"14 26 \n",
|
| 601 |
-
"15 2 \n",
|
| 602 |
-
"16 35 \n",
|
| 603 |
-
"17 2 \n",
|
| 604 |
-
"18 7 \n",
|
| 605 |
-
"19 2 \n",
|
| 606 |
-
"20 20 \n",
|
| 607 |
-
"21 15 \n",
|
| 608 |
-
"22 2 \n",
|
| 609 |
-
"23 2 \n",
|
| 610 |
-
"24 NaN \n",
|
| 611 |
-
"\n",
|
| 612 |
-
" Publications \\\n",
|
| 613 |
-
"0 https://app.dimensions.ai/discover/publication... \n",
|
| 614 |
-
"1 https://app.dimensions.ai/discover/publication... \n",
|
| 615 |
-
"2 https://app.dimensions.ai/discover/publication... \n",
|
| 616 |
-
"3 https://app.dimensions.ai/discover/publication... \n",
|
| 617 |
-
"4 https://app.dimensions.ai/discover/publication... \n",
|
| 618 |
-
"5 https://app.dimensions.ai/discover/publication... \n",
|
| 619 |
-
"6 https://app.dimensions.ai/discover/publication... \n",
|
| 620 |
-
"7 https://app.dimensions.ai/discover/publication... \n",
|
| 621 |
-
"8 https://app.dimensions.ai/discover/publication... \n",
|
| 622 |
-
"9 https://app.dimensions.ai/discover/publication... \n",
|
| 623 |
-
"10 https://app.dimensions.ai/discover/publication... \n",
|
| 624 |
-
"11 https://app.dimensions.ai/discover/publication... \n",
|
| 625 |
-
"12 https://app.dimensions.ai/discover/publication... \n",
|
| 626 |
-
"13 https://app.dimensions.ai/discover/publication... \n",
|
| 627 |
-
"14 https://app.dimensions.ai/discover/publication... \n",
|
| 628 |
-
"15 https://app.dimensions.ai/discover/publication... \n",
|
| 629 |
-
"16 https://app.dimensions.ai/discover/publication... \n",
|
| 630 |
-
"17 https://app.dimensions.ai/discover/publication... \n",
|
| 631 |
-
"18 https://app.dimensions.ai/discover/publication... \n",
|
| 632 |
-
"19 https://app.dimensions.ai/discover/publication... \n",
|
| 633 |
-
"20 https://app.dimensions.ai/discover/publication... \n",
|
| 634 |
-
"21 https://data.4tu.nl/articles/dataset/Receipt_p... \n",
|
| 635 |
-
"22 https://app.dimensions.ai/discover/publication... \n",
|
| 636 |
-
"23 https://app.dimensions.ai/discover/publication... \n",
|
| 637 |
-
"24 NaN \n",
|
| 638 |
-
"\n",
|
| 639 |
-
" Process Discovery/ Declarative Conformance Checking / Alignment / Replay \\\n",
|
| 640 |
-
"0 17 7 \n",
|
| 641 |
-
"1 1 0 \n",
|
| 642 |
-
"2 32 9 \n",
|
| 643 |
-
"3 13 1 \n",
|
| 644 |
-
"4 40 15 \n",
|
| 645 |
-
"5 1 0 \n",
|
| 646 |
-
"6 3 2 \n",
|
| 647 |
-
"7 14 5 \n",
|
| 648 |
-
"8 1 0 \n",
|
| 649 |
-
"9 0 0 \n",
|
| 650 |
-
"10 0 0 \n",
|
| 651 |
-
"11 1 1 \n",
|
| 652 |
-
"12 1 0 \n",
|
| 653 |
-
"13 11 5 \n",
|
| 654 |
-
"14 7 1 \n",
|
| 655 |
-
"15 0 0 \n",
|
| 656 |
-
"16 3 1 \n",
|
| 657 |
-
"17 0 0 \n",
|
| 658 |
-
"18 0 2 \n",
|
| 659 |
-
"19 0 0 \n",
|
| 660 |
-
"20 4 1 \n",
|
| 661 |
-
"21 -1 -1 \n",
|
| 662 |
-
"22 0 0 \n",
|
| 663 |
-
"23 1 0 \n",
|
| 664 |
-
"24 NaN NaN \n",
|
| 665 |
-
"\n",
|
| 666 |
-
" Online / Streaming / Realtime Performance (Analysis) / Temporal / Time \\\n",
|
| 667 |
-
"0 4 1 \n",
|
| 668 |
-
"1 0 1 \n",
|
| 669 |
-
"2 4 8 \n",
|
| 670 |
-
"3 3 4 \n",
|
| 671 |
-
"4 4 13 \n",
|
| 672 |
-
"5 0 0 \n",
|
| 673 |
-
"6 1 2 \n",
|
| 674 |
-
"7 1 1 \n",
|
| 675 |
-
"8 0 0 \n",
|
| 676 |
-
"9 0 0 \n",
|
| 677 |
-
"10 0 0 \n",
|
| 678 |
-
"11 0 0 \n",
|
| 679 |
-
"12 1 0 \n",
|
| 680 |
-
"13 2 14 \n",
|
| 681 |
-
"14 2 0 \n",
|
| 682 |
-
"15 0 1 \n",
|
| 683 |
-
"16 6 6 \n",
|
| 684 |
-
"17 0 1 \n",
|
| 685 |
-
"18 2 2 \n",
|
| 686 |
-
"19 0 0 \n",
|
| 687 |
-
"20 3 1 \n",
|
| 688 |
-
"21 -1 -1 \n",
|
| 689 |
-
"22 0 0 \n",
|
| 690 |
-
"23 0 0 \n",
|
| 691 |
-
"24 NaN NaN \n",
|
| 692 |
-
"\n",
|
| 693 |
-
" Predict(ive)/ Monitoring/ Prescriptive Trace clustering / Clustering \\\n",
|
| 694 |
-
"0 8 2 \n",
|
| 695 |
-
"1 1 0 \n",
|
| 696 |
-
"2 15 1 \n",
|
| 697 |
-
"3 12 4 \n",
|
| 698 |
-
"4 46 0 \n",
|
| 699 |
-
"5 1 0 \n",
|
| 700 |
-
"6 0 0 \n",
|
| 701 |
-
"7 7 0 \n",
|
| 702 |
-
"8 0 0 \n",
|
| 703 |
-
"9 0 0 \n",
|
| 704 |
-
"10 1 0 \n",
|
| 705 |
-
"11 3 0 \n",
|
| 706 |
-
"12 0 0 \n",
|
| 707 |
-
"13 23 1 \n",
|
| 708 |
-
"14 8 0 \n",
|
| 709 |
-
"15 0 0 \n",
|
| 710 |
-
"16 9 4 \n",
|
| 711 |
-
"17 2 0 \n",
|
| 712 |
-
"18 3 0 \n",
|
| 713 |
-
"19 0 0 \n",
|
| 714 |
-
"20 8 0 \n",
|
| 715 |
-
"21 -1 -1 \n",
|
| 716 |
-
"22 1 0 \n",
|
| 717 |
-
"23 0 0 \n",
|
| 718 |
-
"24 NaN NaN \n",
|
| 719 |
-
"\n",
|
| 720 |
-
" Preprocessing / Event Abstraction / Event Data Correlation \\\n",
|
| 721 |
-
"0 2 \n",
|
| 722 |
-
"1 0 \n",
|
| 723 |
-
"2 2 \n",
|
| 724 |
-
"3 1 \n",
|
| 725 |
-
"4 1 \n",
|
| 726 |
-
"5 0 \n",
|
| 727 |
-
"6 3 \n",
|
| 728 |
-
"7 2 \n",
|
| 729 |
-
"8 0 \n",
|
| 730 |
-
"9 0 \n",
|
| 731 |
-
"10 0 \n",
|
| 732 |
-
"11 3 \n",
|
| 733 |
-
"12 0 \n",
|
| 734 |
-
"13 1 \n",
|
| 735 |
-
"14 2 \n",
|
| 736 |
-
"15 0 \n",
|
| 737 |
-
"16 1 \n",
|
| 738 |
-
"17 0 \n",
|
| 739 |
-
"18 0 \n",
|
| 740 |
-
"19 0 \n",
|
| 741 |
-
"20 0 \n",
|
| 742 |
-
"21 -1 \n",
|
| 743 |
-
"22 0 \n",
|
| 744 |
-
"23 0 \n",
|
| 745 |
-
"24 NaN \n",
|
| 746 |
-
"\n",
|
| 747 |
-
" Further keywords: \n",
|
| 748 |
-
"0 (machine) learning, (online process) monitorin... \n",
|
| 749 |
-
"1 (machine) learning, cloud computing \n",
|
| 750 |
-
"2 alarm-based prescriptive process monitoring, b... \n",
|
| 751 |
-
"3 (compliance) monitoring, (machine) learning, d... \n",
|
| 752 |
-
"4 (in)frequent patterns in process models, (mach... \n",
|
| 753 |
-
"5 (in)frequent patterns in process models, (mach... \n",
|
| 754 |
-
"6 (in)frequent patterns in process models \n",
|
| 755 |
-
"7 (machine) learning, rule mining \n",
|
| 756 |
-
"8 privacy preservation, security \n",
|
| 757 |
-
"9 (machine) learning, hidden Markov models \n",
|
| 758 |
-
"10 specification-driven predictive business proce... \n",
|
| 759 |
-
"11 (machine) learning \n",
|
| 760 |
-
"12 automation \n",
|
| 761 |
-
"13 (machine) learning, alarm-based prescriptive p... \n",
|
| 762 |
-
"14 (machine) learning, automation \n",
|
| 763 |
-
"15 stage-based process performance analysis \n",
|
| 764 |
-
"16 (online process) monitoring, remaining time pr... \n",
|
| 765 |
-
"17 (machine) learning, remaining time prediction \n",
|
| 766 |
-
"18 (machine) learning, remaining time prediction \n",
|
| 767 |
-
"19 multi-perspective \n",
|
| 768 |
-
"20 (machine) learning, drift detection \n",
|
| 769 |
-
"21 NaN \n",
|
| 770 |
-
"22 predictions with a-priori knowledge \n",
|
| 771 |
-
"23 multidimensional process mining, process cubes \n",
|
| 772 |
-
"24 NaN "
|
| 773 |
-
]
|
| 774 |
-
},
|
| 775 |
-
"execution_count": 4,
|
| 776 |
-
"metadata": {},
|
| 777 |
-
"output_type": "execute_result"
|
| 778 |
-
}
|
| 779 |
-
],
|
| 780 |
-
"source": [
|
| 781 |
-
"#import pm4py\n",
|
| 782 |
-
"import pandas as pd\n",
|
| 783 |
-
"INPUT_PATH = \"../data/mappings.csv\"\n",
|
| 784 |
-
"df = pd.read_csv(INPUT_PATH, sep = \";\", dtype = \"unicode\")\n",
|
| 785 |
-
"df"
|
| 786 |
-
]
|
| 787 |
-
},
|
| 788 |
-
{
|
| 789 |
-
"cell_type": "code",
|
| 790 |
-
"execution_count": null,
|
| 791 |
-
"id": "04a97f37",
|
| 792 |
-
"metadata": {},
|
| 793 |
-
"outputs": [],
|
| 794 |
-
"source": []
|
| 795 |
-
}
|
| 796 |
-
],
|
| 797 |
-
"metadata": {
|
| 798 |
-
"kernelspec": {
|
| 799 |
-
"display_name": "Python 3 (ipykernel)",
|
| 800 |
-
"language": "python",
|
| 801 |
-
"name": "python3"
|
| 802 |
-
},
|
| 803 |
-
"language_info": {
|
| 804 |
-
"codemirror_mode": {
|
| 805 |
-
"name": "ipython",
|
| 806 |
-
"version": 3
|
| 807 |
-
},
|
| 808 |
-
"file_extension": ".py",
|
| 809 |
-
"mimetype": "text/x-python",
|
| 810 |
-
"name": "python",
|
| 811 |
-
"nbconvert_exporter": "python",
|
| 812 |
-
"pygments_lexer": "ipython3",
|
| 813 |
-
"version": "3.10.7"
|
| 814 |
-
}
|
| 815 |
-
},
|
| 816 |
-
"nbformat": 4,
|
| 817 |
-
"nbformat_minor": 5
|
| 818 |
-
}
|
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|
notebooks/.ipynb_checkpoints/test_feed-checkpoint.ipynb
DELETED
|
The diff for this file is too large to render.
See raw diff
|
|
|
notebooks/benchmarking_process_discovery.ipynb
CHANGED
|
@@ -1277,7 +1277,7 @@
|
|
| 1277 |
"\n",
|
| 1278 |
"import sys\n",
|
| 1279 |
"import os\n",
|
| 1280 |
-
"sys.path.append(os.path.dirname(\"../
|
| 1281 |
"from io_helpers import get_keys_abbreviation\n",
|
| 1282 |
"\n",
|
| 1283 |
"print(benchmarked_ft.shape, benchmarked_pd.shape)\n",
|
|
@@ -1422,7 +1422,7 @@
|
|
| 1422 |
"name": "python",
|
| 1423 |
"nbconvert_exporter": "python",
|
| 1424 |
"pygments_lexer": "ipython3",
|
| 1425 |
-
"version": "3.9.
|
| 1426 |
}
|
| 1427 |
},
|
| 1428 |
"nbformat": 4,
|
|
|
|
| 1277 |
"\n",
|
| 1278 |
"import sys\n",
|
| 1279 |
"import os\n",
|
| 1280 |
+
"sys.path.append(os.path.dirname(\"../gedi/utils/io_helpers.py\"))\n",
|
| 1281 |
"from io_helpers import get_keys_abbreviation\n",
|
| 1282 |
"\n",
|
| 1283 |
"print(benchmarked_ft.shape, benchmarked_pd.shape)\n",
|
|
|
|
| 1422 |
"name": "python",
|
| 1423 |
"nbconvert_exporter": "python",
|
| 1424 |
"pygments_lexer": "ipython3",
|
| 1425 |
+
"version": "3.9.19"
|
| 1426 |
}
|
| 1427 |
},
|
| 1428 |
"nbformat": 4,
|
notebooks/bpic_generability_pdm.ipynb
CHANGED
|
@@ -1223,7 +1223,7 @@
|
|
| 1223 |
"from scipy.stats import pearsonr\n",
|
| 1224 |
"import sys\n",
|
| 1225 |
"import os\n",
|
| 1226 |
-
"sys.path.append(os.path.dirname(\"../
|
| 1227 |
"from io_helpers import get_keys_abbreviation\n",
|
| 1228 |
"\n",
|
| 1229 |
"\n",
|
|
|
|
| 1223 |
"from scipy.stats import pearsonr\n",
|
| 1224 |
"import sys\n",
|
| 1225 |
"import os\n",
|
| 1226 |
+
"sys.path.append(os.path.dirname(\"../gedi/utils/io_helpers.py\"))\n",
|
| 1227 |
"from io_helpers import get_keys_abbreviation\n",
|
| 1228 |
"\n",
|
| 1229 |
"\n",
|
notebooks/experiment_generator.ipynb
CHANGED
|
@@ -2225,7 +2225,7 @@
|
|
| 2225 |
],
|
| 2226 |
"source": [
|
| 2227 |
"bpic_features = pd.read_csv(\"../data/34_bpic_features.csv\", index_col=None)\n",
|
| 2228 |
-
"#bpic_features = pd.read_csv(\"../
|
| 2229 |
"\n",
|
| 2230 |
"#bpic_features = bpic_features.drop(['Unnamed: 0'], axis=1)\n",
|
| 2231 |
"print(bpic_features.shape)\n",
|
|
@@ -3102,7 +3102,7 @@
|
|
| 3102 |
"name": "python",
|
| 3103 |
"nbconvert_exporter": "python",
|
| 3104 |
"pygments_lexer": "ipython3",
|
| 3105 |
-
"version": "3.9.
|
| 3106 |
}
|
| 3107 |
},
|
| 3108 |
"nbformat": 4,
|
|
|
|
| 2225 |
],
|
| 2226 |
"source": [
|
| 2227 |
"bpic_features = pd.read_csv(\"../data/34_bpic_features.csv\", index_col=None)\n",
|
| 2228 |
+
"#bpic_features = pd.read_csv(\"../gedi/output/features/real_event_logs.csv\", index_col=None)\n",
|
| 2229 |
"\n",
|
| 2230 |
"#bpic_features = bpic_features.drop(['Unnamed: 0'], axis=1)\n",
|
| 2231 |
"print(bpic_features.shape)\n",
|
|
|
|
| 3102 |
"name": "python",
|
| 3103 |
"nbconvert_exporter": "python",
|
| 3104 |
"pygments_lexer": "ipython3",
|
| 3105 |
+
"version": "3.9.19"
|
| 3106 |
}
|
| 3107 |
},
|
| 3108 |
"nbformat": 4,
|
notebooks/feature_distributions.ipynb
CHANGED
|
@@ -1847,7 +1847,7 @@
|
|
| 1847 |
"name": "python",
|
| 1848 |
"nbconvert_exporter": "python",
|
| 1849 |
"pygments_lexer": "ipython3",
|
| 1850 |
-
"version": "3.9.
|
| 1851 |
}
|
| 1852 |
},
|
| 1853 |
"nbformat": 4,
|
|
|
|
| 1847 |
"name": "python",
|
| 1848 |
"nbconvert_exporter": "python",
|
| 1849 |
"pygments_lexer": "ipython3",
|
| 1850 |
+
"version": "3.9.19"
|
| 1851 |
}
|
| 1852 |
},
|
| 1853 |
"nbformat": 4,
|