Natalia Sidorova

Dr. Natalia Sidorova is assistant professor at the PA group. She actively works on topics related to process modeling and verification. The application domains include business processes and distributed systems. She has published more than 70 conference and journal papers. She is active in the Health and Wellbeing Action Line of EIT ICT Labs, taking lead of projects towards the development of innovative services for disease prevention making use of modern sensor technologies together with mining, conformance analysis, prediction and recommendation techniques.

Position: UD
Room: MF 7.105
Tel (internal): 3705
Links: Courses
External assignments
Presentations
Projects
Publications
External links: Personal home page
Google scholar page
Scopus page
DBLP page
TU/e page

Recent courses

  • DASU20 – Data Acquisition and Visualization - Links DASU20 / 2019Q2 @ Osiris Staff involved
  • 2IAB0 Data analytics for engineers - Learning goals Students gain insight in basic techniques for processing large amounts of data in an efficient, reliable, and consistent way. Students develop skills in understanding, interpreting, and documenting data and information in the context of realistic scenarios. Students get understanding of the data life cycle and develop skills for structuring their solutions of practical Read More ...
  • 2IMI15 Metamodelling and Interoperability - Independently developed applications based on different models and implemented on different platforms need to use each others services and share each others data. Interoperability is therefore one of the buzz-words of the last years in Computer Science. Web services-driven Service-Oriented Architectures (SOA) have arisen as a solution to the interoperability problem. In this context, metamodeling Read More ...

Recent external assignments

  • Data-driven Product Design at Philips Healthcare (3 Master projects) - The study of user behaviour is an important part of the product design process. This process is particularly more difficult when dealing with products that require very complex user interaction. Therefore, obtaining as much product usage information as possible is needed, since it can reveal patterns in the user behaviour that indicate a misalignment between Read More ...

Recent presentations

Recent projects

  • Certif-AI - Certif-AI: Certification of production process quality through Artificial Intelligence Description Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as Read More ...
  • TACTICS - TACTICS – Techniques for the Analysis of Client-Team InteraCtionS Description In various care and service settings (e.g. mental healthcare, youth care, social work), teams of professionals interact with clients to improve their well-being. The TACTICS project aims at the development of automated techniques to generate insights into the evolving statuses of such clients as well Read More ...
  • Philips Flagship - Description The Data Science Centre Eindhoven (DSC/e) is TU/e’s response to the growing volume and importance of data and the need for data & process scientists (http://www.tue.nl/dsce/). The DSC/e has recently started a long-term strategic cooperation with Philips Research Eindhoven on three topics: data science, health and lighting. As a first concrete action, 70 PhD Read More ...

Recent publications

  • Guided interaction exploration and performance analysis in artifact-centric process models - van Eck, M. L., Sidorova, N., & van der Aalst, W. M. P. (2019). Guided interaction exploration and performance analysis in artifact-centric process models. Business and Information Systems Engineering, 61(6), 649-663. https://doi.org/10.1007/s12599-018-0546-0 Abstract Artifact-centric process models aim to describe complex processes as a collection of interacting artifacts. Recent development in process mining allow for the Read More ...
  • Mining insights from weakly-structured event data - Tax, N. (2019). Mining insights from weakly-structured event data Eindhoven: Technische Universiteit Eindhoven
  • Multi-instance mining: discovering synchronisation in artifact-centric processes - van Eck, M. L., Sidorova, N., & van der Aalst, W. M. P. (2019). Multi-instance mining: discovering synchronisation in artifact-centric processes. In F. Daniel, Q. Z. Sheng, & H. Motahari (Eds.), Business Process Management Workshops – BPM 2018 International Workshops, Revised Papers (pp. 18-30). (Lecture Notes in Business Information Processing; Vol. 342). Cham: Springer. DOI: Read More ...
  • Data-driven usability test scenario creation - van Eck, M. L., Markslag, E., Sidorova, N., Brosens-Kessels, A., & van der Aalst, W. M. P. (2019). Data-driven usability test scenario creation. In M. K. Lárusdóttir, M. Winckler, K. Kuusinen, P. Palanque, & C. Bogdan (Eds.), Human-Centered Software Engineering – 7th IFIP WG 13.2 International Working Conference, HCSE 2018, Revised Selected Papers (pp. 88-108). Read More ...
  • Mining local process models with constraints efficiently: applications to the analysis of smart home data - Tax, N., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2018). Mining local process models with constraints efficiently: applications to the analysis of smart home data. In Proceedings of the 14th International Conference on Intelligent Environments (IE) (pp. 56-63). [8595032] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/IE.2018.00016 Abstract Sequential pattern Read More ...
  • Generating time-based label refinements to discover more precise process models - Tax, N., Alasgarov, E. E., Sidorova, N., Haakma, R., & van der Aalst, W. M. P. (2019). Generating time-based label refinements to discover more precise process models. Journal of Ambient Intelligence and Smart Environments, 11(2), 165-182. DOI: 10.3233/AIS-190519 Abstract Process mining is a research field focused on the analysis of event data with the aim Read More ...
  • Local process model discovery : bringing petri nets to the pattern mining world - Tax, Niek, Sidorova, Natalia, van der Aalst, Wil M.P. & Haakma, Reinder (2018). Local process model discovery : bringing petri nets to the pattern mining world. In V. Khohamenko & O.H. Roux (Eds.), Application and Theory of Petri Nets and Concurrency (pp. 374-384). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence Read More ...
  • Interest-driven discovery of local process models - Tax, Niek, Dalmas, Benjamin, Sidorova, Natalia, van der Aalst, Wil M.P. & Norre, Sylvie (2018). Interest-driven discovery of local process models. Information Systems, 77, 105-117. Abstract Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process Read More ...
  • The imprecisions of precision measures in process mining - Tax, N., Lu, X., Sidorova, N., Fahland, D. & van der Aalst, W.M.P. (2018). The imprecisions of precision measures in process mining. Information Processing Letters, 135, 1-8. Abstract In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been Read More ...
  • Discovering more precise process models from event logs by filtering out chaotic activities - Tax, N., Sidorova, N. & van der Aalst, W.M.P. (2019). Discovering more precise process models from event logs by filtering out chaotic activities. Journal of Intelligent Information Systems, 52(1), 107-139. DOI: 10.1007/s10844-018-0507-6 Abstract Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that Read More ...

 

 

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