The Business Process Management Game
Remco Dijkman and Sander Peters
The Business Process Management Game is a serious game that teaches various aspects of business process management. Students can play the game in groups. Acting as the management team of a business unit, they have to design a business process for that unit that is optimal in terms of cost, customer satisfaction, and waiting and service times. Groups compete with each other to create the process that performs best. In doing so, they can practice their business process modeling, analysis, re-design, and mining skills. The game got much positive feedback from students in official student evaluations of a course in which it is used.
Action Logger: Enabling Process Mining for Robotic Process Automation
Volodymyr Leno1, Artem Polyvyanyy, Marcello La Rosa, Marlon Dumas, and Fabrizio Maria Maggi
This paper presents a tool, called Action Logger, for recording user interface (UI) logs, i.e., logs of user interactions with information systems. By generating output suitable for process mining, the tool aims to introduce process mining methods, techniques, and tools for supporting Robotic Process Automation (RPA) activities, e.g., robot discovery and implementation. Action Logger offers unique capabilities, including logging relevant user actions at a granularity level suitable for RPA, data-awareness, and context-independence.
https://www.youtube.com/watch?v=SvPuOdWfByc&feature=youtu.be and https://github.com/apromore/RPA_UILogger
ACD2: a Tool to Interactively Explore Business Process Logs
Stephen Pauwels and Toon Calders
ACD2 is a tool for detecting anomalies and concept drifts in Business process logs. In contrast to many other existing algorithms, ACD2 does not require any manual parameter to be set. ACD2 is based on Extended Dynamic Bayesian Networks. These models are constructed automatically using machine learning, but can be revised by the user afterwards. This model can then be used for scoring cases. Our tool visually represents these scores, making it easy for a user to investigate the data.
Filtering Toolkit: Interactively Filter Event Logs to Improve the Quality of Discovered Models
AU: Mohammadreza Fani Sani, Alessandro Berti, Sebastiaan J. van Zelst, and Wil van der Aalst
AB: Process discovery algorithms discover process models on the basis of event data automatically. These techniques tend to consider the entire log to discover a process model. However, real-life event logs usually contain outlier behaviour that lead to incomprehensible, complex and inaccurate process models where correct and/or important behaviour is undetectable. Hence, removing outlier behaviour thanks to filtering techniques is an essential step to retrieve a good quality process model. Manually filtering the event log is tricky and requires a significant amount of time. On the other hand, some work in the past is focused on providing a fully automatic choice of the parameters of the discovery and filtering algorithms; however, the attempts were not completely successful. This demo paper describes an easy-to-use plug-in in the ProM process mining framework, that provides a view where several process discovery and outlier filtering algorithms can be chosen, along with their parameters, in order to find a sweet spot leading to a â€™goodâ€™ process model. The filtered log is easily accessible, and the process model is shown inside the view, in this way the user can immediately evaluate the quality of the chosen combination between process discovery and filtering algorithms, and is effectively assisted in the choice of the preprocessing methodology. Some commonly used metrics (fitness, precision) are reported in the view provided by the plug-in, in order to ease the evaluation of the process model. With the options provided by our plug-in, the difficulties of both fullymanual and automatic choice of the filtering approach are effectively overcome.
https://www.youtube.com/watch?v=T31sLvfQD0E&feature=youtu.be and https://drive.google.com/file/d/11xUup9CL5gWA-6PY05MIPqwRc0g50_RZ/view?usp=sharing
Simod: A Tool for Automated Discovery of Business Process Simulation Models
Manuel Camargo, Marlon Dumas, and Oscar GonzÃ¡lez-Rojas
Business process simulation is a widespread approach for quantitative analysis of business processes. However, the creation of accurate business process simulation models is a laborious and error-prone task, due to the numerous parameters that need to be carefully tuned. Additionally, the accuracy of a simulation model is inherently limited by the accuracy of the process model that is used as a starting point. This paper presents Simod: A tool to automatically generate simulation models from event logs. Simod uses an automated process discovery technique to extract a process model from an event log and then enhances this model with simulation parameters extracted via a combination of trace alignment, replay, and curve-fitting techniques. The tool incorporates a Bayesian hyperparameter optimization technique to fine-tune the accuracy of the resulting simulation model.
https://www.youtube.com/watch?v=i9X5jwjuipk&feature=youtu.be and https://github.com/AdaptiveBProcess/Simod
Integrated, Ubiquitous and Collaborative Process Mining with Chat Bots
Within the process mining field we are witnessing a tremendous growth of applications and development frameworks available to perform data analyses. Such growth, which is very positive and desirable, comes with the cost of learning each new tool and difficulties in integrating different systems in order to complement the analyses. In addition, we are noticing the lack of tools enabling collaboration among the users involved in a project. Finally, we think it would be highly recommended to enable ubiquitous processing of data. This paper proposes a solution to all these issues by presenting a chat bot which can be included in discussions to enable the execution of process mining directly from the chat.
https://www.youtube.com/watch?v=eg8jJ3bB0NI&feature=youtu.be and https://github.com/delas/pmbot/wiki
RePROSitory: a Repository platform for sharing business PROcess modelS
Flavio Corradini, Fabrizio Fornari, Andrea Polini, Barbara Re, and Francesco Tiezzi
The BPM community can certainly benefit from the adoption of open science principles. The availability of business process models can make BPM research results more controllable, replicable, and comparable. Unfortunately, in our experience, it is quite difficult to find open collections of models suitable to effectively validate research proposals in the BPM field. To address this issue, we have developed a web-based repository of process models, named RePROSitory, for sharing BPMN models, making them accessible to the community. We have started to systematically populate the repository with a collection of BPMN models, manually selected from the literature. The experience of models retrieval from RePROSitory is enhanced by the implementation of more than two hundreds quality metrics. These allow researchers to select from RePROSitory a set of models that they judge more suitable for the experiments they want to run.
https://www.youtube.com/watch?v=MCYmV9sCREc&feature=youtu.be and https://drive.google.com/file/d/1288cI4Ge2rb7krhbQv9BhkO0fKY-etgC/view?usp=sharing
Nirdizati 2.0: New Features and Redesigned Backend
Williams Rizzi, Luca Simonetto, Chiara Di Francescomarino, Chiara Ghidini, TÃµnis Kasekamp, Fabrizio Maria Maggi
Nirdizati is a dedicated tool for Predictive Process Monitoring, a field of Process Mining that aims at predicting how an ongoing execution of a business process will develop in the future using past process executions recorded in event logs. Nirdizati is a web application supporting users in building, comparing, and analyzing predictive models that can then be used to perform predictions on the future of an ongoing case. By providing a rich set of different state-of-the-art approaches, Nirdizati offers BPM researchers and practitioners a useful and flexible instrument for investigating and comparing Predictive Process Monitoring techniques. In this paper, we present a Nirdizati version with a redesigned backend, which improves its modularity and scalability, and with new features, which further enrich its capability to support researchers and practitioners to deal with different monitoring tasks.
https://drive.google.com/open?id=1ar7psIbXJckceubsTiNsAuTl_UpI0WjC and https://drive.google.com/open?id=14h5UWNQ1y1ghAevBfmhXaeSLi-fDqqSU
ELPaaS: Event Log Privacy as a Service
Martin Bauer, Stephan A. Fahrenkrog-Petersen, Agnes Koschmider, Felix Mannhardt, Han van der Aa, Matthias Weidlich
The privacy of an organizationâ€™s workers represents a crucial concern in process mining settings, where data on an individualâ€™s performance is recorded and possibly shared for analysis. To enable users to appropriately deal with privacy concerns in process mining, this paper introduces ELPaaS (Event Log Privacy as a Service), a web application that offers state-of-the-art techniques for event log sanitization and privacy-preserving process mining queries. By employing our techniques, users obtain event logs and process mining results that provide privacy guarantees such as differential privacy and k-anonymity. Hence, the privacy of an organizationâ€™s workers is protected.
https://www.youtube.com/watch?v=XLq124VpZ6Q&feature=youtu.be and https://github.com/samadeusfp/ELPaaS/raw/master/tutorial_elpaas.pdf
Online Comparison of Streaming Process Discovery Algorithms
Kavya Baskar, Marwan Hassani
In the active field of process mining, several techniques have been proposed in various areas like process discovery and conformance
checking. The integration of data stream mining techniques in process mining has gained popularity in recent years. The ProM framework that enables process mining with streaming data has been advanced to support event streams in the recent past. In this paper we present a new extension that is built upon existing work related to obtaining process models from data streams within ProM. The extension enables researchers to visually compare the results of two different process discovery algorithms for a single incoming stream of events with different algorithms to deal with the data streams such as Lossy Counting with Budget, Sliding Window and Exponential Decay.
https://www.youtube.com/watch?v=2eMZe6NWhW0&feature=youtu.be and https://www.dropbox.com/s/yh2rzymgy3higqn/Tutorial_Online_Comparison_of_Streaming_Process_Discovery_Algorithms_%28BPM_2019_Demo%29.pdf?dl=0
A Tool for Decision Logic Verification in DMN Decision Tables
Carl Corea, Jonas Blatt, Patrick Delfmann
The Decision Model and Notation (DMN) is a popular standard to model company decision logic. Here, decision tables can be used to specify decision logic by the means of business rules. As these tables are modelled and maintained in an incremental and collaborative manner, this raises the need to verify the correctness of DMN decision tables. In this report, we therefore present a tool which allows to analyze the decision logic in DMN decision tables at design-time. Our tool implements all so-called verification capabilities from the recently proposed â€business rule management capability frameworkâ€ by Smit et al. , and also allows to detect errors distributed among multiple tables.
https://www.youtube.com/watch?v=yTXTKi3s6LM&feature=youtu.be and https://gitlab.uni-koblenz.de/fg-bks/br-verification-tool/wikis/home
PM4Py Web Services: Easy Development, Integration and Deployment of Process Mining Features in any Application Stack
Alessandro Berti, Sebastiaan J. van Zelst, Wil van der Aalst
In recent years, process mining emerged as a set of techniques to analyze process data, supported by different open-source and commercial solutions. Process mining tools aim to discover process models from the data, perform conformance checking, predict the future behavior of the process and/or provide other analyses that enhance the overall process knowledge. Additionally, commercial vendors provide integration with external software solutions, facilitating the external use of their process mining algorithms. This integration is usually established
by means of a set of web services that are accessible from an external software stack. In open-source process mining stacks, only a few solutions provide a corresponding web service. However, extensive documentation is often missing and/or tight integration with the front-end of the tool hampers the integration of the services with other software. Therefore, in this paper, a new open-source Python process mining service stack, PM4Py-WS, is presented. The proposed software supports easy integration with any software stack, provides an extensive documentation of the API and a clear separation between the business logic, (graphical) interface and the services. The aim is to increase the integration of process mining techniques in business intelligence tools.
https://www.youtube.com/watch?time_continue=3&v=g1B4YUeW8Lg and https://drive.google.com/file/d/1289uYdTPXe8u5EaiZ8WGEDuhSILn9QDL/view?usp=sharing
bpmpatterns.org – An Interactive Catalog of Business Process Modeling Patterns Literature
Ralf Laue, Agnes Koschmider, Michael Fellmann, Andreas Schoknecht, Arthur Vetter
Patterns descriptions of proven and well-documented solutions for recurring problems have gained widespread interest and acceptance in the area of business process modeling. In the past years, a large number of such patterns have been documented in the literature. However, it is still difficult to find patterns that can be useful in a given context. The reason is that the relevant publications are spread in various journals and other types of publications, and there is no guidance for locating a pattern that can be useful for solving a given problem. In this demo, we present an interactive web-site that provides a comprehensive overview on published work in the field of business process modeling patterns. It allows finding publications on business process modeling patterns based on various search criteria. It is intended to be useful both for business process modeling practitioners as for researchers in need of sound literature references. Currently, this catalog (meant to be a growing resource) provides an categorization of 95 publications on patterns as well as 50 publication on anti-patterns.
http://www.bpmpatterns.org/files/demo.mp4 and http://www.bpmpatterns.org/files/tutorial.pdf
Process Attribute Visualization in 3D and Virtual Reality
Manuel Gall, Stefanie Rinderle-Ma
Being able to visually explore process attributes and their values supports process analysts in process understanding and optimization. The complexity of the analysis can range from a few to a variety of attributes, e.g., machining times and sensor parameter in the manufacturing domain. This paper introduces an innovative prototype to visualize process attributes within a 3D representation. The 3D representation can be displayed and explored on a monitor or a virtual reality system.
http://gruppe.wst.univie.ac.at/~gallm6/uploads/BPM2019/ and http://gruppe.wst.univie.ac.at/~gallm6/uploads/BPM2019/BPM_Tutorial_3D_Process_Visualization.pdf
POMElog: Generating Event Logs from Unplugged Processes
Luis Leiva, Jorge Munoz-Gama, Juan Salas-Morales, Victor Galvez, Wai Lam Jonathan Lee, Rene de La Fuente, Ricardo Fuentes, Marcos SepÃºlveda
We consider unplugged processes to be those processes that are not supported by any information system. In order to analyze them using Process Mining techniques, those processes could be video recorded and then watch the recordings to create an event log. POMElog was developed as a simple user-friendly process-aware tool to aid a domain expert on manually tagging unplugged processes recordings. The tool allows to create events, update times, and export the event logs generated. Also, the tool has been used in real cases, specially to generate event logs for surgical procedures in the medical education domain.