Merge branch 'bpm24' into 8-experiments-organise-experimental-data-to-reproduce-paper-figures

* bpm24:
Inlcude bpm24 in CI
Update README.md
Delete data/mappings.csv
Anonymization
Update README.md

.github/workflows/test_gedi.yml

@@ -5,6 +5,7 @@ on:
   pull_request:
     branches:
       - main
+      - bpm24
 
 # Specifies the jobs that are to be run
 jobs:

README.md

@@ -47,4 +47,4 @@ For reference of possible keys and values for each step, please see `config_file
 To run the whole pipeline please create a new `.json` file, specifying all steps you want to run and specify desired keys and values for each step. 
 
 ## References
-The framework used by `GEDI` is taken directly from the original paper by [Maldonado](mailto:andreamalher.[email protected]), Frey, Tavares, Rehwald and Seidl. If you would like to discuss the paper, or corresponding research questions on benchmarking process mining tasks please email the authors.
+The framework used by `GEDI` is taken directly from the original paper by [...]. If you would like to discuss the paper, or corresponding research questions on benchmarking process mining tasks please email the authors.

data/mappings.csv

@@ -1,26 +0,0 @@
-Name;Short description;data link;challenge link;Citations (Stand Februar 2023);Publications;Process Discovery/ Declarative;Conformance Checking / Alignment / Replay;Online / Streaming / Realtime;Performance (Analysis) / Temporal / Time;Predict(ive)/  Monitoring/ Prescriptive;Trace clustering / Clustering;Preprocessing / Event Abstraction / Event Data Correlation;Further keywords:
-Sepsis Cases - Event Log;This real-life event log contains events of sepsis cases from a hospital. Sepsis is a life threatening condition typically caused by an infection. One case represents the pathway through the hospital.;https://data.4tu.nl/articles/dataset/Sepsis_Cases_-_Event_Log/12707639;https://data.4tu.nl/articles/dataset/Sepsis_Cases_-_Event_Log/12707639;61;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A915d2bfb-7e84-49ad-a286-dc35f063a460;17;7;4;1;8;2;2;(machine) learning, (online process) monitoring, automation, healthcare, meta-learning, privacy preservation
-BPI 2017 - Offer Log;Contains data from a financial institute including all applications filed and their subsequent handling. There may be multiple offers per application. However, at most one of them should always be accepted.;https://data.4tu.nl/articles/dataset/BPI_Challenge_2017_-_Offer_log/12705737/1;https://www.win.tue.nl/bpi/doku.php?id=2017:challenge;4;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A7e326e7e-8b93-4701-8860-71213edf0fbe;1;0;0;1;1;0;0;(machine) learning, cloud computing
-Road Traffic Fine Management Process (not BPI);A real-life event log taken from an information system of the Italian police. The information system supports the management and handling of road traffic fines by a local police force in Italy.;https://data.4tu.nl/articles/dataset/Road_Traffic_Fine_Management_Process/12683249;;95;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A270fd440-1057-4fb9-89a9-b699b47990f5;32;9;4;8;15;1;2;alarm-based prescriptive process monitoring, business process deviance, incremental process discovery, multi-perspective, ordering, remaining time prediction, variants, workflow net
-BPI 2011;Contains data from from a Dutch Academic Hospital. Each case is a patient of a Gynaecology department. The log contains information about when certain activities took place, which group performed the activity and so on.;https://data.4tu.nl/articles/dataset/Real-life_event_logs_-_Hospital_log/12716513/1;https://www.win.tue.nl/bpi/doku.php?id=2011:challenge;57;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3Ad9769f3d-0ab0-4fb8-803b-0d1120ffcf54;13;1;3;4;12;4;1;(compliance) monitoring, (machine) learning, deviant process instances detection, drift detection, explainable predictions, healthcare, outcome prediction, temporal logic
-BPI 2012;Contains the event log of an application process for a personal loan or overdraft within a global financing organization.;https://data.4tu.nl/articles/dataset/BPI_Challenge_2012/12689204;https://www.win.tue.nl/bpi/doku.php?id=2012:challenge;151;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A3926db30-f712-4394-aebc-75976070e91f;40;15;4;13;46;0;1;(in)frequent patterns in process models, (machine) learning, automation, event-log sampling, explainable predictions, generilazation in process discovery, multi-perspective, next event prediction, privacy preservation, remaining time prediction, sequence model, sequence prediction, simulation, temporal logic
-BPI 2013 - Open Problems;Rabobank Group ICT implemented ITIL processes and tracked events such as changes and incidents;https://data.4tu.nl/articles/dataset/BPI_Challenge_2013_open_problems/12688556/1?file=24026252;https://www.win.tue.nl/bpi/2013/challenge.html;6;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A3537c19d-6c64-4b1d-815d-915ab0e479da;1;0;0;0;1;0;0;(in)frequent patterns in process models, (machine) learning
-BPI 2013 - Closed Problems;Rabobank Group ICT implemented ITIL processes and tracked events such as changes and incidents;https://data.4tu.nl/articles/dataset/BPI_Challenge_2013_closed_problems/12714476/1;https://www.win.tue.nl/bpi/doku.php?id=2013:challenge;12;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3Ac2c3b154-ab26-4b31-a0e8-8f2350ddac11;3;2;1;2;0;0;3;(in)frequent patterns in process models
-BPI 2013 - Incidents;The log contains events from an incident and problem management system called VINST.;https://data.4tu.nl/articles/dataset/BPI_Challenge_2013_incidents/12693914/1;https://www.win.tue.nl/bpi/2013/challenge.html;36;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A500573e6-accc-4b0c-9576-aa5468b10cee;14;5;1;1;7;0;2;(machine) learning, rule mining
-BPI 2014 - Incident Records;Rabobank Group ICT implemented ITIL processes and tracked events such as changes and incidents;https://data.4tu.nl/articles/dataset/BPI_Challenge_2014_Activity_log_for_incidents/12706424;https://www.win.tue.nl/bpi/doku.php?id=2014:challenge;5;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A86977bac-f874-49cf-8337-80f26bf5d2ef;1;0;0;0;0;0;0;privacy preservation, security
-BPI 2014 - Interaction Records;Rabobank Group ICT implemented ITIL processes and tracked events such as changes and incidents;https://data.4tu.nl/articles/dataset/BPI_Challenge_2014_Interaction_details/12692411;https://www.win.tue.nl/bpi/doku.php?id=2014:challenge;1;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A3d5ae0ce-198c-4b5c-b0f9-60d3035d07bf;0;0;0;0;0;0;0;(machine) learning, hidden Markov models
-BPI 2015 - Log 3;Provided by 5 Dutch municipalities. The data contains all building permit applications over a period of approx. 4 years (Municipality 3);https://data.4tu.nl/articles/dataset/BPI_Challenge_2015_Municipality_3/12718370?backTo=/collections/_/5065424;https://www.win.tue.nl/bpi/doku.php?id=2015:challenge;1;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3Aed445cdd-27d5-4d77-a1f7-59fe7360cfbe;0;0;0;0;1;0;0;specification-driven predictive business process monitoring
-BPI 2015 - Log 1;Provided by 5 Dutch municipalities. The data contains all building permit applications over a period of approx. 4 years (Municipality 1);https://data.4tu.nl/articles/dataset/BPI_Challenge_2015_Municipality_1/12709154;https://www.win.tue.nl/bpi/doku.php?id=2015:challenge;8;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3Aa0addfda-2044-4541-a450-fdcc9fe16d17;1;1;0;0;3;0;3;(machine) learning
-BPI 2016 - Clicks Logged In;Contains clicks of users that are logged in from a Dutch autonomous administrative authority that implements employee insurances and provides labour market and data services.;https://data.4tu.nl/articles/dataset/BPI_Challenge_2016_Clicks_Logged_In/12674816;https://www.win.tue.nl/bpi/doku.php?id=2016:challenge;1;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A01345ac4-7d1d-426e-92b8-24933a079412;1;0;1;0;0;0;0;automation
-BPI 2017 - Application Log;Contains data from a financial institute including all applications filed and their subsequent handling. There may be multiple offers per application. However, at most one of them should always be accepted.;https://data.4tu.nl/articles/dataset/BPI_Challenge_2017/12696884/1;https://www.win.tue.nl/bpi/doku.php?id=2017:challenge;73;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A5f3067df-f10b-45da-b98b-86ae4c7a310b;11;5;2;14;23;1;1;(machine) learning, alarm-based prescriptive process monitoring, automation, privacy preservation, remaining time prediction, resource constraints, simulation, trace ordering
-BPI 2018;The process covers the handling of applications for EU direct payments for German farmers from the https://en.wikipedia.org/wiki/European_Agricultural_Guarantee_Fund. It contains yearly allocation of direct payments, starting with the application and, if all goes well, finishing with the authorization of a payment.;https://data.4tu.nl/articles/dataset/BPI_Challenge_2018/12688355/1;https://www.win.tue.nl/bpi/doku.php?id=2018:challenge;26;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A3301445f-95e8-4ff0-98a4-901f1f204972;7;1;2;0;8;0;2;(machine) learning, automation
-BPI 2020 - Travel Permits;Contains 2 years of data from the reimbursement process at TU/e. Travel permits should be approved before making any arrangements.;https://data.4tu.nl/articles/dataset/BPI_Challenge_2020_Travel_Permit_Data/12718178;https://icpmconference.org/2020/bpi-challenge/;2;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3Aea03d361-a7cd-4f5e-83d8-5fbdf0362550;0;0;0;1;0;0;0;stage-based process performance analysis
-BPI 2019;Contains the purchase order handling process of a multinational company operating from the Netherlands in the area of coatings and paints.;https://data.4tu.nl/articles/dataset/BPI_Challenge_2019/12715853/1;https://icpmconference.org/2019/icpm-2019/contests-challenges/bpi-challenge-2019/;35;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3Ad06aff4b-79f0-45e6-8ec8-e19730c248f1;3;1;6;6;9;4;1;(online process) monitoring, remaining time prediction
-BPI 2020 - International Declarations;Contains 2 years of data from the reimbursement process at TU/e. For international trips, permission is needed from the supervisor.;https://data.4tu.nl/articles/dataset/BPI_Challenge_2020_International_Declarations/12687374;https://icpmconference.org/2020/bpi-challenge/;2;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A2bbf8f6a-fc50-48eb-aa9e-c4ea5ef7e8c5;0;0;0;1;2;0;0;(machine) learning, remaining time prediction
-BPI 2020 - Domestic Declarations;Contains 2 years of data from the reimbursement process at TU/e. For domestic trips, no prior permission is needed, i.e. an employee can undertake these trips and ask for reimbursement of the costs afterwards.;https://data.4tu.nl/articles/dataset/BPI_Challenge_2020_Domestic_Declarations/12692543;https://icpmconference.org/2020/bpi-challenge/;7;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A3f422315-ed9d-4882-891f-e180b5b4feb5;0;2;2;2;3;0;0;(machine) learning, remaining time prediction
-BPI 2020 - Prepaid Travel Cost;Contains 2 years of data from the reimbursement process at TU/e. ;https://data.4tu.nl/articles/dataset/BPI_Challenge_2020_Prepaid_Travel_Costs/12696722;https://icpmconference.org/2020/bpi-challenge/;2;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A5d2fe5e1-f91f-4a3b-ad9b-9e4126870165;0;0;0;0;0;0;0;multi-perspective
-Helpdesk;Ticketing management process of the Help desk of an Italian software company;https://data.4tu.nl/articles/dataset/Dataset_belonging_to_the_help_desk_log_of_an_Italian_Company/12675977;;20;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3A0c60edf1-6f83-4e75-9367-4c63b3e9d5bb;4;1;3;1;8;0;0;(machine) learning, drift detection
-Receipt phase of an environmental permit application process (WABO), CoSeLoG project;Data originates from the CoSeLoG project where (dis)similarities between processes of different Dutch municipalities have been investigated. Here: execution of the receiving phase of the building permit application process.;https://data.4tu.nl/articles/dataset/Receipt_phase_of_an_environmental_permit_application_process_WABO_CoSeLoG_project/12709127;;15;https://data.4tu.nl/articles/dataset/Receipt_phase_of_an_environmental_permit_application_process_WABO_CoSeLoG_project/12709127;-1;-1;-1;-1;-1;-1;-1;
-Environmental permit application process (‘WABO’), CoSeLoG project – Municipality 4;Data originates from the CoSeLoG project where (dis)similarities between processes of different Dutch municipalities have been investigated. Here: execution of a building permit application process in one of five anonymous municipalities (municipality 4);https://data.4tu.nl/articles/dataset/Environmental_permit_application_process_WABO_CoSeLoG_project_Municipality_4/12718076?backTo=/collections/_/5065529;;2;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3Ae8c3a53d-5301-4afb-9bcd-38e74171ca32;0;0;0;0;1;0;0;predictions with a-priori knowledge
-Environmental permit application process (‘WABO’), CoSeLoG project – Municipality 1;Data originates from the CoSeLoG project where (dis)similarities between processes of different Dutch municipalities have been investigated. Here: execution of a building permit application process in one of five anonymous municipalities (municipality 1);https://data.4tu.nl/articles/dataset/Environmental_permit_application_process_WABO_CoSeLoG_project_Municipality_1/12714599?backTo=/collections/_/5065529;;2;https://app.dimensions.ai/discover/publication?and_subset_figshare_doi=10.4121%2Fuuid%3Ac45dcbe9-557b-43ca-b6d0-10561e13dcb5;1;0;0;0;0;0;0;multidimensional process mining, process cubes
-;;;;;;;;;;;;;

setup.py

@@ -12,8 +12,8 @@ setup(
         name = 'gedi',
         version = str(version),
         description = 'Generating Event Data with Intentional Features for Benchmarking Process Mining',
-        author = 'Andrea Maldonado',
-        author_email = '[email protected]',
+        author = '...',
+        author_email = '...',
         license = 'MIT',
         url='https://github.com/lmu-dbs/gedi.git',
         long_description=long_description,