Untangling Robotic Process Automation with Process Mining
Robotic Process Automation (RPA) is an emerging automation technology in the field of Business Process Management (BPM) that creates software (SW) robots to partially or fully automate rule-based and repetitive tasks (aka routines) previously performed by human users in their applications’ user interfaces (UIs). Nowadays, successful usage of RPA requires strong support by skilled human experts, from the detection of the routines to be automated to the development of the executable scripts required to enact SW robots. In this tutorial, we discuss how process mining can be leveraged to minimize the manual and time-consuming steps required for the creation of SW robots, enabling new levels of automation and support for RPA. Specifically, after providing an overview of the current RPA practices, we present a pipeline of processing steps that enable us not only to semiautomatically discover the anatomy of a routine directly from the UI logs recording the interactions between workers and SW applications, but also to automatically develop executable scripts for performing SW robots at run-time. We show how this pipeline can be effectively enacted by researchers/practitioners through the SmartRPA tool. In addition, we present a reference data model for UI logs with the aim to standardize their core attributes to enable log sharing among different RPA tools.
Simone Agostinelli received his PhD in Engineering in Computer Science from Sapienza Universià di Roma in 2022. He is currently a postdoctoral research fellow in Engineering in Computer Science at Sapienza Università di Roma. In 2019, he received the Forum Award at the 31st International Conference on Advanced Information Systems Engineering (CAiSE’19). His main research interest focuses on synthesizing strategies for Robotic Process Automation via Process Mining and Automated Planning techniques.
Andrea Marrella is Associate Professor with Sapienza Università di Roma, Italy. His research spans across the wide spectrum of BPM with a particular focus on process adaptation and resilience in cyber-physical environments and trace alignment in process mining. He published more than 90 research papers on the above topics and earned a best paper award at CAiSE 2017 and a best forum paper award at CAiSE 2019. Since 2017, he acts as Information Director of the ACM Journal of Data and Information Quality. In 2020, he was the Guest Editor of the special issue on Artificial Intelligence of Business Process Management for the Journal on Data Semantics (ISSN 1861-2032). Andrea Marrella serves/has served regularly as a reviewer for top class journals and as a member of the Program Committee of prestigious conferences, such as BPM, CAiSE, AAAI, IJCAI, INTERACT, SAC, BIS, AVI, etc. In 2021, he was workshop chair for the BPM’21 conference. In 2022, he is PC chair of the RPA Forum for the BPM’22 conference. Since 2021, he is the principal investigator for Sapienza of the H2020 EU project DataCloud, which focuses on developing novel process mining techniques for the management of big data pipelines.
Luka Abb is a research associate and PhD student at the University of Mannheim. In his research, he develops methods for collecting, modeling, processing, and analyzing user interaction logs, i.e., very detailed process logs that represent the interactions of users with the graphical user interface of a computer system. Potential applications include the analysis of user behavior in software systems, the design of assistance systems and components, and the automated execution of processes in user interfaces.
Jana-Rebecca Rehse is Junior Professor for Management Analytics at the University of Mannheim. Her research focusses on applications of data science and artificial intelligence in process management, in particular data-driven methods for process analysis, process assistance, and process automation. Her research results, funded by the German Research Foundation and the German Ministry of Education and Research, have been published in more than 40 conference and journal papers so far. From 2015 to 2020, she was a researcher and project lead at the German Research Center for Artificial Intelligence. She regularly serves as a reviewer and associate editor for prestigious journals and conferences in the BPM and IS fields.
Jana-Rebecca Rehse is Junior Professor for Management Analytics at the University of Mannheim. Her research focusses on applications of data science and artificial intelligence in process management, in particular data-driven methods for process analysis, process assistance, and process automation. Her research results, funded by the German Research Foundation and the German Ministry of Education and Research, have been published in more than 40 conference and journal papers so far. From 2015 to 2020, she was a researcher and project lead at the German Research Center for Artificial Intelligence. She regularly serves as a reviewer and associate editor for prestigious journals and conferences in the BPM and IS fields.