# Identifying candidate routines for Robotic Process Automation from unsegmented UI logs

arXiv: https://arxiv.org/abs/2008.05782 · HTML: https://mlsanity.com/paper/2008.05782/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data](https://mlsanity.com/paper/2204.04164/) (2022)
- [Resolving Uncertain Case Identifiers in Interaction Logs: A User Study](https://mlsanity.com/paper/2212.00009/) (2022)
- [A Reference Data Model for Process-Related User Interaction Logs](https://mlsanity.com/paper/2207.12054/) (2022)
- [TeleTune: Evolving Agent Skills From Offline Telemetry](https://mlsanity.com/paper/2610.05437/) (2026)

## Abstract (arXiv)

Robotic Process Automation (RPA) is a technology to develop software bots that automate repetitive sequences of interactions between users and software applications (a.k.a. routines). To take full advantage of this technology, organizations need to identify and to scope their routines. This is a challenging endeavor in large organizations, as routines are usually not concentrated in a handful of processes, but rather scattered across the process landscape. Accordingly, the identification of routines from User Interaction (UI) logs has received significant attention. Existing approaches to this problem assume that the UI log is segmented, meaning that it consists of traces of a task that is presupposed to contain one or more routines. However, a UI log usually takes the form of a single unsegmented sequence of events. This paper presents an approach to discover candidate routines from unsegmented UI logs in the presence of noise, i.e. events within or between routine instances that do not belong to any routine. The approach is implemented as an open-source tool and evaluated using synthetic and real-life UI logs.