Anna Kalenkova

Anna Kalenkova

School of Mathematical Sciences

Faculty of Sciences, Engineering and Technology


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 study how process models can represent and visualise event data.

  • Appointments

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

    Year Citation
    2022 Polyvyanyy, A., & Kalenkova, A. (2022). Conformance checking of partially matching processes: An entropy-based approach. Information Systems, 106, 101720.
    DOI
    2021 Tour, A., Polyvyanyy, A., & Kalenkova, A. (2021). Agent system mining: Vision, benefits, and challenges. IEEE Access, 9, 99480-99494.
    DOI
    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
    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 Scopus8
    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 Scopus11
    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 Scopus53
    2012 Kalenkova, A. A. (2012). An algorithm of automatic workflow optimization. Programming and Computer Software, 38(1), 43-56.
    DOI Scopus1
    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
    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). IOS Press.
    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 Conceptual Modeling. ER 2021 Vol. 13011 LNCS (pp. 62-73). Switzerland: Springer International Publishing.
    DOI
    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.
    Scopus3
    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 Scopus4
    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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12168 LNCS (pp. 129-146). New York, NY, USA: Springer International Publishing.
    DOI Scopus2
    2020 Kalenkova, A., Carmona, J., Polyvyanyy, A., & La Rosa, M. (2020). Automated Repair of Process Models Using Non-local Constraints. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12152 LNCS (pp. 280-300). Springer International Publishing.
    DOI
    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 Scopus1
    2019 Skobtsov, A. V., & Kalenkova, A. A. (2019). Using heuristic algorithms for fast alignment between business processes and goals. In Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOCW Vol. 2019-October (pp. 85-91). IEEE.
    DOI
    2019 Skobtsov, A. V., & Kalenkova, A. A. (2019). Efficient comparison of process models using Tabu search algorithm. In CEUR Workshop Proceedings Vol. 2478 (pp. 51-61).
    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 Scopus13
    2018 Tarantsova, P. D., & Kalenkova, A. A. (2018). Constructing regular expressions from real-life event logs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11179 LNCS (pp. 274-280). 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 Scopus9
    2018 Konchagin, A. M., & Kalenkova, A. A. (2018). On the efficient application of aho-corasick algorithm in process mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10716 LNCS (pp. 371-377). 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
    2016 Shershakov, S. A., Kalenkova, A. A., & Lomazova, I. A. (2016). Transition systems reduction: Balancing between precision and simplicity. In CEUR Workshop Proceedings Vol. 1592 (pp. 78-95).
    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).
    Scopus17
    2015 Van Der Aalst, W. M. P., Kalenkova, A., Rubin, V., & Verbeek, E. (2015). Process discovery using localized events. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9115 (pp. 287-308). Springer International Publishing.
    DOI Scopus19
    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).
    Scopus16
    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 Scopus9
    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 Scopus13
    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).

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