Process science and technology

Welcome to the home page of the Process Science and Technology research group. In our research, we use data generated by real-world processes, for example, business processes executed by organisations or physical processes executed and controlled in manufacturing, to understand how the processes were executed in the past and suggest how their future executions can be improved. Our primary research areas are process mining, business process management, and process querying, and we apply our research findings in manufacturing, finance, retail, and healthcare.

Process mining

The research discipline of process mining combines studies of inferences from data in data mining and machine learning with process modelling and analysis to tackle the problems of discovering, monitoring, and improving real-world processes. In process mining, we collect data generated by information systems that support daily operations at organisations and build models that help these organisations understand what they are actually doing based on the evidence in data, including what organisations do well and where they deviate from the desired behaviours and expected performance, and recommend how the future processes can be improved.

Business process management

Business process management (BPM) discipline studies methods, techniques, and tools for modelling, analysing, automating, executing, monitoring, and improving business processes at organisations. A business process, the focal artifact of study in BPM, can be understood as a collection of activities and decisions performed by humans and machines that aim to achieve a goal. In our research, we are primarily interested in designing new methods for managing unstructured and flexible business processes.

Process querying

Process querying combines concepts from Big data and process modelling and analysis with business process intelligence and process analytics to study techniques for retrieving and manipulating models of real-world and envisioned processes to organise and extract process-related information for subsequent systematic use. In process querying, we design and implement methods and techniques for automatically querying large arrays of data generated by real-world processes and models that describe how the processes should be carried out at organisations.

People

Chair

A/Prof Artem Polyvyanyy
artem.polyvyanyy@unimelb.edu.au