Automated Discovery of Data Transformations for Robotic Process Automation
This work addresses the challenge of automating repetitive data transfer tasks for companies using RPA, but it is incremental as it builds on existing techniques with specific optimizations.
The paper tackles the problem of discovering data transfer routines in Robotic Process Automation (RPA) by analyzing User Interaction logs, proposing optimizations to improve computational efficiency over a naive state-of-the-art method.
Robotic Process Automation (RPA) is a technology for automating repetitive routines consisting of sequences of user interactions with one or more applications. In order to fully exploit the opportunities opened by RPA, companies need to discover which specific routines may be automated, and how. In this setting, this paper addresses the problem of analyzing User Interaction (UI) logs in order to discover routines where a user transfers data from one spreadsheet or (Web) form to another. The paper maps this problem to that of discovering data transformations by example - a problem for which several techniques are available. The paper shows that a naive application of a state-of-the-art technique for data transformation discovery is computationally inefficient. Accordingly, the paper proposes two optimizations that take advantage of the information in the UI log and the fact that data transfers across applications typically involve copying alphabetic and numeric tokens separately. The proposed approach and its optimizations are evaluated using UI logs that replicate a real-life repetitive data transfer routine.