Anna Kalenkova

Dr Anna Kalenkova

Lecturer

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD - email supervisor to discuss availability.

I have a background in theoretical computer science and applied mathematics. My interests lie in process mining, event data analysis, visual analytics, and information theory. I apply stochastic modelling in process mining to study how stochastic models can be discovered from event data.

  • Appointments
    Date Position Institution name
    2023 - ongoing Lecturer University of Adelaide
    2022 - 2023 Postdoctoral Research Fellow University of Adelaide
    2019 - 2021 Postdoctoral Research Fellow University of Melbourne

  • Journals
    Year Citation
    2024 Yavorskiy, R., Cavalli, A. R., & Kalenkova, A. (2024). Preface. Communications in Computer and Information Science, 1559, v.
    2022 Kalenkova, A., Mitchell, L., & Roughan, M. (2022). Performance Analysis: Discovering Semi-Markov Models From Event Logs.
    2022 Polyvyanyy, A., & Kalenkova, A. (2022). Conformance checking of partially matching processes: An entropy-based approach. Information Systems, 106, 1-15.
    DOI Scopus8
    2021 Kalenkova, A., Carmona, J., Polyvyanyy, A., & La Rosa, M. (2021). Automated Repair of Process Models with Non-local Constraints Using State-Based Region Theory. Fundamenta Informaticae, 183(3-4), 293-317.
    DOI Scopus2
    2021 Tour, A., Polyvyanyy, A., & Kalenkova, A. (2021). Agent system mining: Vision, benefits, and challenges. IEEE Access, 9, 99480-99494.
    DOI Scopus15 WoS4
    2019 Kalenkova, A. A., & Kolesnikov, D. A. (2019). Application of a Genetic Algorithm for Finding Edit Distances between Process Models. Automatic Control and Computer Sciences, 53(7), 617-627.
    DOI Scopus2
    2019 Kalenkova, A., Burattin, A., de Leoni, M., van der Aalst, W., & Sperduti, A. (2019). Discovering high-level BPMN process models from event data. Business Process Management Journal, 25(5), 995-1019.
    DOI Scopus16 WoS10
    2017 Mitsyuk, A. A., Shugurov, I. S., Kalenkova, A. A., & van der Aalst, W. M. P. (2017). Generating event logs for high-level process models. Simulation Modelling Practice and Theory, 74, 1-16.
    DOI Scopus22 WoS14
    2017 Kalenkova, A. A., van der Aalst, W. M. P., Lomazova, I. A., & Rubin, V. A. (2017). Process mining using BPMN: relating event logs and process models. Software and Systems Modeling, 16(4), 1019-1048.
    DOI Scopus67 WoS44
    2012 Kalenkova, A. A. (2012). An algorithm of automatic workflow optimization. Programming and Computer Software, 38(1), 43-56.
    DOI Scopus1 WoS1
    2010 Kalenkova, A. A. (2010). Application of if-conversion to verification and optimization of workflows. Programming and Computer Software, 36(5), 276-288.
    DOI Scopus3 WoS3
    - Kalenkova, A. A., & Kolesnikov, D. A. (n.d.). Application of Genetic Algorithms for Finding Edit Distance between Process Models. Modeling and Analysis of Information Systems, 25(6), 711-725.
    DOI

  • Books
    Year Citation
    2021 van der Aalst, W. M. P., Batagelj, V., Buzmakov, A., Ignatov, D. I., Kalenkova, A., Khachay, M., . . . Tutubalina, E. (2021). Preface (Vol. 1357 CCIS).
    2021 Kalenkova, A., Lozano, J., & Yavorskiy, R. (2021). Preface (Vol. 1288 CCIS).
    2021 van der Aalst, W. M. P., Batagelj, V., Buzmakov, A., Ignatov, D., Kalenkova, A., Khachay, M., . . . Tutubalina, E. (2021). Preface (Vol. 12602 LNCS). W. van der Aalst, E. Best, & W. Penczek (Eds.), SAGE Publications.
    DOI

  • Book Chapters
    Year Citation
    2023 Tour, A., Polyvyanyy, A., Kalenkova, A., & Senderovich, A. (2023). Agent Miner: An Algorithm for Discovering Agent Systems from Event Data. In G. Goos (Ed.), Lecture Notes in Computer Science (Vol. 14159 LNCS, pp. 284-302). Springer Nature Switzerland.
    DOI Scopus1
    2017 Shershakov, S. A., Kalenkova, A. A., & Lomazova, I. A. (2017). Transition systems reduction: Balancing between precision and simplicity. In M. Koutny, J. Kleijn, & W. Penczek (Eds.), Transactions on Petri Nets and Other Models of Concurrency XII (Vol. 10470, pp. 119-139). Berlin, Heidelbert: Springer.
    DOI

  • Conference Papers
    Year Citation
    2021 Jans, M., Janssenswillen, G., Kalenkova, A., & Maggi, F. M. (2021). Preface to the ICPM 2021 Doctoral Consortium and Tool Demonstration Track. In CEUR Workshop Proceedings Vol. 3098.
    2021 Kalenkova, A., Polyvyanyy, A., & Rosa, M. L. (2021). Structural and Behavioral Biases in Process Comparison Using Models and Logs. In Proceedings of the International Conference on Conceptual Modeling (CCM 2021), as published in Lecture Notes in Computer Science Vol. 13011 (pp. 62-73). Cham, Switzerland: Springer International Publishing.
    DOI Scopus1
    2020 Polyvyanyy, A., Alkhammash, H., Di Ciccio, C., García-Bañuelos, L., Kalenkova, A., Leemans, S. J. J., . . . Weidlich, M. (2020). Entropia: A family of entropy-based conformance checking measures for process mining. In CEUR Workshop Proceedings Vol. 2703 (pp. 39-42). RWTH Aachen University: CEUR-WS.
    Scopus8
    2020 Kalenkova, A., & Polyvyanyy, A. (2020). A spectrum of entropy-based precision and recall measurements between partially matching designed and observed processes. In Service-Oriented Computing: 18th International Conference, ICSOC 2020, Dubai, United Arab Emirates, December 14–17, 2020, Proceedings Vol. 12571 LNCS (pp. 337-354). Switzerland: Springer International Publishing.
    DOI Scopus7
    2020 Kalenkova, A., Polyvyanyy, A., & La Rosa, M. (2020). A framework for estimating simplicity of automatically discovered process models based on structural and behavioral characteristics. In Proceedings of the 18th International Conference, Business Process Management (BPM 2020) as published in Lecture Notes in Computer Science Vol. 12168 LNCS (pp. 129-146). New York, NY, USA: Springer International Publishing.
    DOI Scopus5 WoS2
    2020 Kalenkova, A., Carmona, J., Polyvyanyy, A., & La Rosa, M. (2020). Automated Repair of Process Models Using Non-local Constraints. In Proceedings of the 41st International Conference on Applications and Theory of Petri Nets and Concurrency (PETRI NETS 2020), as published in Lecture Notes in Computer Science Vol. 12152 LNCS (pp. 280-300). Manhattan, New York City, USA: Springer International Publishing.
    DOI Scopus8 WoS4
    2019 Skobtsov, A., & Kalenkova, A. (2019). Efficient algorithms for finding differences between process models. In 2019 Ivannikov Ispras Open Conference, (ISPRAS) (pp. 60-66). Piscataway, New Jersey, USA: IEEE.
    DOI Scopus2
    2019 Skobtsov, A. V., & Kalenkova, A. A. (2019). Using heuristic algorithms for fast alignment between business processes and goals. In Proceedings 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop (EDOCW) Vol. 2019-October (pp. 85-91). Piscataway, NJ, USA: IEEE.
    DOI Scopus1
    2019 Skobtsov, A. V., & Kalenkova, A. A. (2019). Efficient comparison of process models using Tabu search algorithm. In I. Lomazova, A. Kalenkova, & R. Yavorsky (Eds.), Proceedings of the MACSPro Workshop 2019 (MACSPro 2019) Vol. 2478 (pp. 51-61). Aachen, Germany: CEUR.
    Scopus1
    2019 Polyvyanyy, A., & Kalenkova, A. (2019). Monotone conformance checking for partially matching designed and observed processes. In Proceedings - 2019 International Conference on Process Mining, ICPM 2019 (pp. 81-88). Piscataway, New Jersey, NJ, USA: IEEE.
    DOI Scopus18
    2018 Tarantsova, P. D., & Kalenkova, A. A. (2018). Constructing regular expressions from real-life event logs. In W. M. P. van der Aalst, V. Batagelj, G. Glavas, D. I. Ignatov, M. Khachay, & S. O. Kuznetsov (Eds.), Conference proceedings Analysis of Images, Social Networks and Texts Vol. 11179 LNCS (pp. 274-280). Switzerland: Springer International Publishing.
    DOI
    2018 Kalenkova, A. A., Ageev, A. A., Lomazova, I. A., & van der Aalst, W. M. P. (2018). E-government services: Comparing real and expected user behavior. In E. Teniente, & M. Weidlich (Eds.), Business Process Management Workshops Vol. 308 (pp. 484-496). New York City, USA: Springer International Publishing.
    DOI Scopus12 WoS4
    2018 Konchagin, A. M., & Kalenkova, A. A. (2018). On the efficient application of aho-corasick algorithm in process mining. In Conference proceedings Analysis of Images, Social Networks and Texts Vol. 10716 LNCS (pp. 371-377). Switzerland: Springer International Publishing.
    DOI
    2017 Shershakov, S. A., Kalenkova, A. A., & Lomazova, I. A. (2017). Transition systems reduction: Balancing between precision and simplicity. In Transactions on Petri Nets and Other Models of Concurrency XII Vol. 10470 LNCS (pp. 119-139). Berlin Heidelberg: Springer.
    DOI Scopus2
    2015 Ivanov, S. Y., Kalenkova, A. A., & Van Der Aalst, W. M. P. (2015). BPMNDiffViz: A tool for BPMN Models comparison?. In CEUR Workshop Proceedings Vol. 1418 (pp. 35-39).
    Scopus22
    2015 Van Der Aalst, W. M. P., Kalenkova, A., Rubin, V., & Verbeek, E. (2015). Process discovery using localized events. In R. Devillers, & A. Valmari (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9115 (pp. 287-308). Brussels, BELGIUM: SPRINGER-VERLAG BERLIN.
    DOI Scopus22 WoS18
    2014 Kalenkova, A. A., De Leoni, M., & Van Der Aalst, W. M. P. (2014). Discovering, analyzing and enhancing BPMN models using ProM. In CEUR Workshop Proceedings Vol. 1295 (pp. 36-40).
    Scopus18
    2014 Kalenkova, A. A., & Lomazova, I. A. (2014). Discovery of cancellation regions within process mining techniques. In Fundamenta Informaticae Vol. 133 (pp. 197-209). IOS PRESS.
    DOI Scopus13 WoS7
    2014 Kalenkova, A. A., Lomazova, I. A., & Van Der Aalst, W. M. P. (2014). Process model discovery: A method based on transition system decomposition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 8489 LNCS (pp. 71-90). Springer International Publishing.
    DOI Scopus18
    2013 Kalenkova, A. A., & Lomazova, I. A. (2013). Discovery of cancellation regions within process mining techniques. In CEUR Workshop Proceedings Vol. 1032 (pp. 232-244).
    Scopus4
    2011 Ataeva, O. M., Kalenkova, A. A., & Serebriakov, V. A. (2011). MultiMeta - A system of spatial and digital library resources integration. In CEUR Workshop Proceedings Vol. 803 (pp. 26-29).

  • Current Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2024 Co-Supervisor Developing explainable AI methods for the financial sector Doctor of Philosophy Doctorate Full Time Mr Wenrui Zhang
    2023 Principal Supervisor Analysis of users' behaviours in social networks using process mining techniques Master of Philosophy Master Full Time Mr Ethan Michael Johnson

  • Past Higher Degree by Research Supervision (University of Adelaide)
    Date Role Research Topic Program Degree Type Student Load Student Name
    2022 - 2023 Co-Supervisor Measuring and modelling information flows in real-world networks Master of Philosophy Master Full Time Miss Bridget Anna Smart
    2022 - 2024 Co-Supervisor A multi-dimensional extension of the Elo rating system Doctor of Philosophy Doctorate Full Time Mr Adam Hugh Hamilton
  • Position: Lecturer
  • Email: anna.kalenkova@adelaide.edu.au
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor Level Five
  • Room: 5.38
  • Org Unit: School of Computer and Mathematical Sciences
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