Grant: Discovery Grants Program - Individual

Process Mining (PM) is a data science approach that supports the discovery, analysis, monitoring, and improvement of existing processes (e.g., business or clinical) by extracting knowledge from event logs produced by information systems. PM is valuable because it offers data-driven insights about process executions as they are, rather than what people assume they are. PM software is becoming increasingly popular, with a market size growing from $2.3B in 2023 to an expected $38B by 2030. Yet, there are still major hurdles that plague the success of PM projects, and many are related to several categories of requirements that they entail. Requirements Engineering (RE) focuses on the elicitation, modelling, analysis, specification, validation, and management of requirements from stakeholders, for systems and software. There is a recent trend to have data-driven RE approaches, but little research is available on the synergy between PM and RE. In this context, an important question is how can RE and PM be better integrated in a framework that promotes 1) the development of valid requirements for PM-based process analysis, improvement, and automation, and 2) the use of PM in support of evidence-driven RE activities? The long-term research objective of this research program is to answer this question by producing a combined RE-PM framework, called Requirements-oriented Process Mining (RoPM) and composed of methods, algorithms, and tools, which targets the RE-based development of PM and automation projects, and the use of PM to inform RE activities. In particular, it aims to 1) develop a method to identify contextual requirements needed for successful integration between PM and Robotic Process Automation (RPA) and low-code platforms; 2) develop a visualization of mined processes that enables architectural/hierarchical visualization and traceability to goals; 3) investigate requirements-driven privacy-protected PM, with a focus on synthetic log generation; 4) provide pre-processing tool support for privacy-oriented features and log synthesis; and 5) validate the results of the four previous objectives in Canadian contexts (e.g., government services, education, industry, healthcare), iteratively, with real applications. The expected outcomes include effective RE methods, notations, and tools for PM and process automation, which consider privacy, equity, diversity, and inclusion, with validation against real processes from different Canadian sectors. This RoPM program is original and timely as Canada lags in PM adoption. It is also important as it will enable the synergetic improvement of RE and PM practices in industrial and public sectors and indirectly lead to more successful PM/automation initiatives in Canada and elsewhere, resulting in more effective processes that will save money and improve the lives of process users. This program will also help a dozen graduate students develop research and technical skills that will be highly in demand.