Multi-Perspective process model discovery for robotic process automation

Abstract

Robotic Process Automation (RPA) is a novel approach for immediate cost reduction and gaining operational efficiency. RPA tools can automate repeatable tasks, thus reducing the error rates and increasing overall process performance. Even more, RPA improves the quality of the data (data completeness, data consistency/correctness, etc.). Although, being widely used in many organizations, RPA suffers from high time consumption allocated to the training of software robots (bots for short). Moreover, the models used for training are often inaccurate, which leads to increase of time spent on testing the bots. One of the possible solutions is to apply process mining in order to extract the information about the processes from UI logs such as clickstreams and keylogs, which can then be used to train the bots. However, traditional process discovery techniques are not suitable for the purpose of RPA, as they discover only control-flow perspective of the process and cannot deal well with the UI logs, producing huge and complex models. The proposed research project aims at shifting process mining techniques from working on event logs to working on UI logs as well as developing multi-perspective automated discovery technique, which can then be applied to train the RPA bots.