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Discovering bot-flow for robotic process automation

Science and Engineering Faculty student topic

Study level

PhD

Master of Philosophy

Honours

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.

Supervisors

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

Overview

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)

Outcomes

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

Contact the supervisor for more information.