Doctoral defence: Volodymyr Leno “Robotic Process Mining: accelerating the adoption of Robotic Process Automation”

On 17 January at 9:00 Volodymyr Leno will defend his doctoral thesis “Robotic Process Mining: accelerating the adoption of Robotic Process Automation” for obtaining the degree of Doctor of Philosophy (in Computer Science).

Supervisors:
Prof. Marlon Dumas,  University of Tartu
Assoc. Prof. Fabrizio Maria Maggi, Free University of Bozen-Bolzano (Italy)
Prof. Marcello La Rosa, University of Melbourne (Australia)
Senior Lecturer Artem Polyvyanyy, University of Melbourne (Australia)

Opponents:
Assoc. Prof. Paolo Ceravolo, University of Milan (Italy)
Assoc. Prof. Carmelo del Valle, University of Seville (Spain)

Summary
Robotic Process Automation (RPA) is a technology to automate repetitive tasks. Using an RPA tool, a user can develop a software bot that can execute a sequence of interactive steps with one or more software applications. For example, we can use an RPA tool to develop a software bot that opens a spreadsheet, then opens a Web form, and then copy-pastes data from one of the cells in the spreadsheet into one of the fields in the Web form.

While RPA tools allow users to automate many types of tasks, they do not help users to determine which tasks are candidates for automation. The current practice for identifying routines that are suitable for RPA automation is by means of interviews and observation of workers conducting their daily work. This manual approach is time-consuming and is not cost-efficient, especially in organizations that have a very large number of routines.

In this thesis, we propose a set of techniques, which we call Robotic Process Mining (RPM) techniques, to discover automatable routines from recordings of interactions between one or more workers and one or more software applications. The goal of RPM techniques is to help business analysts to draw a systematic inventory of the tasks that can be automated in an organization. In addition, these techniques can produce scripts (programs) to automate each automatable task.

The techniques proposed in this thesis have been consolidated in an open-source tool called Robidium. Robidium starts by recording one or more work sessions of several hours from one or more workers in an organization. Robidium then analyzes these recordings to discover sequences of steps that are frequently repeated (routines). Each routine is analyzed to determine if it can be automated using a software bot. When possible, Robidium produces a software bot capable of performing each routine automatically.

The defence will be held in Zoom: https://ut-ee.zoom.us/j/99620571442?pwd=MUpQekErSUpKeFlsa1BDRW1ROW5xQT09