researcher, process mining, data mining

Roy Jing Yang 杨靖

researcher, process mining, data mining

About me

  • I’m a 3rd-year PhD student at Queensland University of Technology (QUT), Australia. I’m interested in discovering knowledge from process execution data to support improved decision-making, especially knowledge about (human) resources.
  • My current research focuses on mining organizational models from process execution data and applying them for workforce analytics (read more).
  • I’m also involved in a Food Agility CRC project investigating how process modeling and predictive process analytics can be applied to facilitate complex decisions in crop production and sales planning.
  • I’m a member of the Explainable Analytics for Machine Intelligence (XAMI) Lab at QUT.

Education

  • PhD (current), Queensland University of Technology, Australia
    Thesis title: “Discovering Organizational Models from Event Logs for Workforce Analytics,
    advised by Dr. Chun Ouyang, Prof. Arthur ter Hofstede, and Prof. Wil van der Aalst.
  • M.E. (2019), Sun Yat-sen University, China
    Thesis title: “An Organizational Mining Method for Supporting Business Process Redesign”,
    advised by Prof. Yang Yu.
  • B.E. (2016), Sun Yat-sen University, China

Honors and Awards

  • Australian Commonwealth Research Training Program (Scholarship), 2019-2022
  • Food Agility CRC Top-up Scholarship, 2021-2022
  • Excellent Master Student Award, Sun Yat-sen University, 2018
  • Meritorious Winner (Team 37861) the Mathematical Contest in Modelling, Consortium for Mathematics and Its Applications (COMAP), 2015

Selected papers

  1. Jing Yang, Chun Ouyang, Arthur H. M. ter Hofstede, and Wil M. P. van der Aalst (2022). No Time to Dice: Learning Execution Contexts from Event Logs for Resource-Oriented Process Mining. BPM 2022 (accepted)
  2. Jing Yang, Chun Ouyang, Wil M. P. van der Aalst, Arthur H. M. ter Hofstede, and Yang Yu (2022). OrdinoR: A Framework for Discovering, Evaluating, and Analyzing Organizational Models using Event Logs. Decis. Support Syst. [ doi | pdf ]
  3. Jing Yang, Chun Ouyang, Guvenc Dik, Paul Corry, and Arthur H. M. Hofstede (2022). Crop Harvest Forecast via Agronomy-Informed Process Modelling and Predictive Monitoring. CAiSE 2022 [ doi | pdf ]
  4. Jing Yang, Chun Ouyang, Arthur H. M. ter Hofstede, Wil M. P. van der Aalst, and Michael Leyer (2021). Seeing the Forest for the Trees: Group-Oriented Workforce Analytics. BPM 2021 [ doi | pdf ]