Discovering data transfer routines from user interaction logs. Issue 107 (July 2022)

• Record Type: Journal Article
• Authors: Leno, Volodymyr; Augusto, Adriano; Dumas, Marlon; La Rosa, Marcello; Maggi, Fabrizio Maria; Polyvyanyy, Artem
• Is Part Of: Information systems. Issue 107(2022)
• Publication Date: 2022-07
• Subjects: Robotic process automation -- Robotic process mining -- UI log; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2021.101916
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: Robotic Process Automation (RPA) is a technology to automate routine work such as copying data across applications or filling in document templates using data from multiple applications. RPA tools allow organizations to automate a wide range of routines. However, identifying and scoping routines that can be automated using RPA tools is time consuming. Manual identification of candidate routines via interviews, walk-throughs, or job shadowing allow analysts to identify the most visible routines, but these methods are not suitable when it comes to identifying the long tail of routines in an organization. This article proposes an approach to discover automatable routines from logs of user interactions with IT systems and to synthetize executable specifications for such routines. The proposed approach focuses on discovering routines where a user transfers data from a set of fields (or cells) in an application, to another set of fields in the same or in a different application (data transfer routines). The approach starts by discovering frequent routines at a control-flow level (candidate routines). It then determines which of these candidate routines are automatable and it synthetizes an executable specification for each such routine. Finally, it identifies semantically equivalent routines so as to output a set of non-redundant routines. The article reports on an evaluation of the approach using a combination of synthetic and real-life logs. The evaluation results show that the approach can discover automatable routines that are known to be present in a UI log, and that it discovers routines that users recognize as such in real-life logs. Highlights: This paper presents an approach to discover executable specifications for RPA. The approach takes as input a log of User Interactions from one or more process workers. The approach produces an executable specification that can be compiled into an RPA bot. The approach is evaluated on synthetic and real-life logs. The evaluation reports on its execution times and ability to accurately discover routines. The evaluation shows that the approach is able to rediscover routines present in a log. The approach identifies automatable routines that users recognize as such in real-life logs.