3.8 Proceedings Paper

Mastering Robotic Process Automation with Process Mining

Journal

BUSINESS PROCESS MANAGEMENT (BPM 2022)
Volume 13420, Issue -, Pages 47-53

Publisher

SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-031-16103-2_6

Keywords

Robotic Process Automation; Process mining; User Interface (UI) logs; Reference data model for UI logs; Segmentation; Automated generation of SW robots from UI Logs; SmartRPA

Funding

  1. H2020 project DataCloud
  2. Sapienza grant BPbots

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This paper discusses how process mining can be used to minimize manual and time-consuming steps in RPA, enabling more automation in the creation of software robots. The paper presents a reference data model and a pipeline of processing steps, and demonstrates the implementation of this pipeline using the SmartRPA tool.
Robotic Process Automation (RPA) is an emerging automation technology 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). 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 paper, 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. We first present a reference data model that can be used for a standardized specification of UI logs recording the interactions between workers and SW applications to enable interoperability among different tools. Then, we introduce a pipeline of processing steps that enable us to (1) semi-automatically discover the anatomy of a routine directly from the UI logs, and (2) 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.

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