POSTDOC SUPERVISION

Dr Zahra Dasht Bozorgi (from Jul 10, 2023 to now, principal supervisor), The University of Melbourne, Australia

Dr Anna Kalenkova (from Aug 5, 2019 to Dec 31, 2021, principal supervisor), The University of Melbourne, Australia

PHD SUPERVISION

Tian Li (Oct 1, 2023 to now, principal supervisor), The University of Melbourne, Australia

Anandi Karunaratne (Sep 5, 2023 to now, principal supervisor), The University of Melbourne, Australia

Qingtan Shen (Aug 4, 2023 to now, principal supervisor), The University of Melbourne, Australia

Thakshila Dilrukshi (Nov 21, 2022 to now, principal supervisor), The University of Melbourne, Australia

Wenjun Zhou (Jul 11, 2022 to now, principal supervisor), The University of Melbourne, Australia

Hanan Alkhammash (Nov 9, 2020 to now, principal supervisor), The University of Melbourne, Australia

Andrei Tour (Aug 31, 2020 to now, principal supervisor), The University of Melbourne, Australia

Zihang Su (Aug 26, 2020 to now, principal supervisor), The University of Melbourne, Australia

Zahra Bozorgi (Mar 2, 2020 onwards, co-supervisor, Prescriptive Analytics of Business Processes Using Causal Inference), The University of Melbourne, Australia

Volodymyr Leno (Aug 29, 2018 to Feb 7, 2022, co-supervisor, Robotic Process Mining: Accelerating the Adoption of Robotic Process Automation), The University of Melbourne, Australia

Anuruddha De Alwis (Feb 27, 2017 to Jun 30, 2021, co-supervisor, Microservice-Based Reengineering of Enterprise Systems for Cloud Migration), Queensland University of Technology, Brisbane, Australia

RESEARCH INTERESTS

My research interests span areas like Computing Systems, Information Systems, Distributed Systems, Process Modeling and Analysis, Process Science, Process Mining, and Algorithms. For more information on my research, please take a look at my publications.

I am continuously looking for talented and motivated candidates to perform PostDoc and PhD projects under my supervision. Below, I detail several areas of my research. Contact me for more information on available projects and scholarships.

Process Mining: Process Mining combines studies of inferences from data in Data Mining and Machine Learning with Process Modeling and Analysis to tackle the problems of discovering, monitoring, and improving real-world processes. A selection of my publications on proces mining is listed below.

Artem Polyvyanyy and Anna Kalenkova
Conformance Checking of Partially Matching Processes: An Entropy-Based Approach   [postprint]
Information Systems (IS), 106, 101720, 2022, Elsevier.

Artem Polyvyanyy, Alistair Moffat, and Luciano García-Bañuelos
An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining   [postprint]  [presentation]
Proceedings of the 2nd International Conference on Process Mining (ICPM)
Padua, Italy, October 4–9, 2020. IEEE.
Errata: The graph in Figure 7(c) is incorrect. The correct version of it shows a similar pattern to Figures 7(a) and 7(b), and will appear in 2021 in a journal version of this paper (we hope).

Artem Polyvyanyy, Andreas Solti, Matthias Weidlich, Claudio Di Ciccio, and Jan Mendling
Monotone Precision and Recall Measures for Comparing Executions and Specifications of Dynamic Systems   [postprint]
ACM Transactions on Software Engineering and Methodology (TOSEM), 29(3), pp. 17:1–17:41, 2020, ACM, NY, USA.

Process Querying: Process Querying combines concepts from Big Data and Process Modeling and Analysis with Business Process Intelligence and Process Analytics to study techniques for retrieving and manipulating models of real-world and envisioned processes to organize and extract process-related information for subsequent systematic use. A selection of my publications on proces querying is listed below.

Artem Polyvyanyy, Anastasiia Pika, and Arthur H. M. ter Hofstede
Scenario-Based Process Querying for Compliance, Reuse, and Standardization   [postprint]
Information Systems (IS), 93, 2020, Elsevier.

Artem Polyvyanyy, Chun Ouyan, Alistair Barros, and Wil M.P. van der Aalst
Process Querying: Enabling Business Intelligence through Query-Based Process Analytics   [postprint]
Decision Support Systems (DSS), 100, pp. 41–56, 2017, Elsevier.

Artem Polyvyanyy, Marcello La Rosa, and Arthur H.M. ter Hofstede
Indexing and Efficient Instance-Based Retrieval of Process Models Using Untanglings   [postprint]
Proceedings of the 26th International Conference on Advanced Information Systems Engineering (CAiSE)
Thessaloniki, Greece, June 16-20, 2014. LNCS 8484, pp. 439-456, Springer International Publishing.