English

Automated Discovery of Data Transformations for Robotic Process Automation

Artificial Intelligence 2020-01-07 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2001.01007,
  title  = {Automated Discovery of Data Transformations for Robotic Process Automation},
  author = {Volodymyr Leno and Marlon Dumas and Marcello La Rosa and Fabrizio Maria Maggi and Artem Polyvyanyy},
  journal= {arXiv preprint arXiv:2001.01007},
  year   = {2020}
}

Comments

8 pages, 5 figures. To be published in proceedings of AAAI-20 workshop on Intelligent Process Automation

R2 v1 2026-06-23T13:02:39.989Z