A Review of Data-driven Robotic Process Automation Exploiting Process Mining

Purpose: Process mining aims to construct, from event logs, process maps that can help discover, automate, improve, and monitor organizational processes. Robotic process automation (RPA) uses software robots to perform some tasks usually executed by humans. It is usually difficult to determine what processes and steps to automate, especially with RPA. Process mining is seen as one way to address such difficulty. This paper aims to assess the applicability of process mining algorithms in accelerating and improving the implementation of RPA, along with the challenges encountered throughout project lifecycles. Methodology: A systematic literature review was conducted to examine the approaches where process mining techniques were used to understand the as-is processes that can be automated with software robots. Eight databases were used to identify papers on this topic. Findings: A total of 19 papers, all published since 2018, were selected from 158 unique candidate papers and then analyzed. There is an increase in the number of publications in this domain. Originality: The literature currently lacks a systematic review that covers the intersection of process mining and robotic process automation. The literature mainly focuses on the methods to record the events that occur at the level of user interactions with the application, and on the preprocessing methods that are needed to discover routines with the steps that can be automated. Several challenges are faced with preprocessing such event logs, and many lifecycle steps of automation project are weakly supported by existing approaches.Comment: 29 pages, 5 figures, 5 table

Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs

Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering

Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs

Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). By using Design Science Research, this paper introduces a new method, called cloud pattern API-process mining (CPA-PM), which enables the discovery and analysis of cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface, with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Applying CPA-PM to a case with real and evolving event logs related to the trial process of a software-as-a-service cloud application led to useful analyses and insights, with reusable scripts. CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering