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Discovering bot-flow for robotic process automation

Science and Engineering Faculty student topic

Study level


Master of Philosophy


Vacation research experience scheme

Faculty/Lead unit

Science and Engineering Faculty

School of Information Systems

Topic status

We're looking for students to study this topic.


Dr Suriadi Lim
Senior Lecturer in Business Process Management
Division / Faculty
Science and Engineering Faculty


Robotic Process Automation (RPA) is a technology that use computer programs called “bots” to mimic the sequence of actions taken by a human through a series of computer-based applications in order to accomplish a particular task. The sequence of actions that bots mimic is referred to as ``bot-flow’‘. A ``bot-flow’’ is typically comprised of various computer-human interactions, such as keyboard strokes, mouse clicks, and launching an application.

Currently, a design blueprint of a bot-flow is needed in order develop a bot. Such a blueprint is largely obtained through manual efforts by observing and mapping human actions into some process models. The reliant on manual efforts is not ideal as it is time-consuming and it may not be as comprehensive due to limited observation time window.

Fortunately, today’s IT systems have the capability to record user interaction data (i.e. data related to a human’s actions in his/her interactions with computer applications). This research project aims to develop algorithms and techniques that can be used to automatically discover a bot-flow from low-level user interaction data. A key challenge in this research is the vast volume of user interaction data which may contain a high level of random user activities that are not related to the main task being performed, as well as the interleaving of activities when users were multi-tasking.

Research activities

Research activities in this project vary, depending on students’ skills and duration of the project. In general, research activities for this project include:

  • Literature review on the state-of-the-art techniques that attempt to discover bot-flow from low-level user interaction data (VRES)
  • The investigation of the applicability of various data mining techniques that can discriminate between task-relevant activities and non-task-relevant activities from user interaction log (VRES, Honours)
  • Development of new data mining techniques to isolate task-relevant data (Masters, PhD)
  • The development, implementation, and evaluation of algorithms to discover bot-flow from user interaction log (PhD)


The expected project outcomes are dependent on the scope of the project that students’ undertake. Key outcomes include:

  • Gap analysis in the domain of bot-flow discovery
  • Evaluation of the suitability of existing data mining techniques to discriminate, from user interaction log, relevant and non-relevant user activity with respect to a particular task
  • New techniques that can isolate relevant user activities with respect to a particular task,
  • Techniques that can semi-automatically discover bot-flows that are expressed as process models.

Skills and experience

  • Familiarity with the fields of data mining, data science, process mining, process automation
  • Reasonable writing skills
  • Problem-solving and logical thinking capabilities
  • Computer programming skills


Contact the supervisor for more information.