Francesca Zerbato
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Ph.D.
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Zerbato
First name
Francesca
Email
francesca.zerbato@unisg.ch
Phone
+41 71 224 26 69
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  • Publication
    The biggest business process management problems to solve before we die
    (Elsevier, 2023-01)
    Beerepoot, Iris
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    Ciccio, Claudio Di
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    Reijers, Hajo A.
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    Rinderle-Ma, Stefanie
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    Bandara, Wasana
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    Burattin, Andrea
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    Calvanese, Diego
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    Chen, Tianwa
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    Cohen, Izack
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    Depaire, Benoît
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    Federico, Gemma Di
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    Dumas, Marlon
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    van Dun, Christopher
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    Fehrer, Tobias
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    Fischer, Dominik A.
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    Gal, Avigdor
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    Indulska, Marta
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    Isahagian, Vatche
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    Klinkmüller, Christopher
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    Kratsch, Wolfgang
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    Leopold, Henrik
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    Looy, Amy Van
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    Lopez, Hugo
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    Lukumbuzya, Sanja
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    Mendling, Jan
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    Meyers, Lara
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    Moder, Linda
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    Montali, Marco
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    Muthusamy, Vinod
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    Reichert, Manfred
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    Rizk, Yara
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    Rosemann, Michael
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    Röglinger, Maximilian
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    Sadiq, Shazia
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    Seiger, Ronny orcid-logo
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    Slaats, Tijs
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    Simkus, Mantas
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    Someh, Ida Asadi
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    Weber, Barbara orcid-logo
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    Weber, Ingo
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    Weske, Mathias
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    Zerbato, Francesca
    It may be tempting for researchers to stick to incremental extensions of their current work to plan future research activities. Yet there is also merit in realizing the grand challenges in one’s field. This paper presents an overview of the nine major research problems for the Business Process Management discipline. These challenges have been collected by an open call to the community, discussed and refined in a workshop setting, and described here in detail, including a motivation why these problems are worth investigating. This overview may serve the purpose of inspiring both novice and advanced scholars who are interested in the radical new ideas for the analysis, design, and management of work processes using information technology.
    Type: journal article
    Journal: Computers in Industry
    Volume: 146
    URL: https://www.sciencedirect.com/science/article/pii/S0166361522002330
    DOI: https://doi.org/10.1016/j.compind.2022.103837
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/107840
  • Publication
    Process mining for healthcare: Characteristics and challenges
    ( 2022)
    Munoz-Gama, Jorge
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    Martin, Niels
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    Fernandez-Llatas, Carlos
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    Johnson, Owen A.
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    Sepúlveda, Marcos
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    Helm, Emmanuel
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    Galvez-Yanjari, Victor
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    Rojas, Eric
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    Martinez-Millana, Antonio
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    Aloini, Davide
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    Amantea, Ilaria Angela
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    Andrews, Robert
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    Arias, Michael
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    Beerepoot, Iris
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    Benevento, Elisabetta
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    Burattin, Andrea
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    Capurro, Daniel
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    Carmona, Josep
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    Comuzzi, Marco
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    Dalmas, Benjamin
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    and Chiara Ghidini and Fernanda Gonzalez-Lopez and Gema Ibanez-S, Rene de la Fuenteand Chiara Di Francescomarinoand Claudio Di Cic
    ;
    Vanwersch, Rob
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    Weske, Mathias
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    Wynn, Moe Thandar
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    Zerbato, Francesca
    Type: journal article
    Journal: Journal of Biomedical Informatics
    Volume: 127
    URL: https://www.sciencedirect.com/science/article/pii/S1532046422000107
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/109444
  • Publication
    Granularity in Process Mining: Can We Fix It?
    ( 2021-09)
    Zerbato, Francesca
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    Seiger, Ronny orcid-logo
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    Di Federico, Gemma
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    Burattin, Andrea
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    Weber, Barbara orcid-logo
    Process mining techniques rely on the availability of event logs, where events have a certain granularity that is deemed appropriate for representing business activities. In this paper, we discuss why choosing a proper granularity level during preprocessing can be challenging and reflect on the implications that such a “fixed” view over the process bears for the analysis. Then, inspired by use cases in the context of user behavior analysis, we envision possible solutions that allow exploring and mining multiple granularity levels of process activities.
    Type: conference paper
    Journal: CEUR Workshop Proceedings
    Volume: 2938
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/110067
  • Publication
    Towards IoT-Driven Process Event Log Generation for Conformance Checking in Smart Factories
    ( 2020-09)
    Seiger, Ronny orcid-logo
    ;
    Zerbato, Francesca
    ;
    Burattin, Andrea
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    García-Bañuelos, Luciano
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    Weber, Barbara orcid-logo
    The Internet of Things (IoT) enables software-based access to vast amounts of data streams from sensors measuring physical and virtual properties of smart devices and their surroundings. While sophisticated means for the control and data analysis of single IoT devices exist, a more process-oriented view of IoT systems is often missing. Such a lack of process awareness hinders the development of process-based systems on top of IoT environments and the application of process mining techniques for process analysis and optimization in IoT. We propose a framework for the stepwise correlation and composition of raw IoT sensor streams with events and activities on a process level based on Complex Event Processing (CEP). From this correlation we derive refined process event logs–possibly with ambiguities–that can be used for process analysis at runtime (i. e., online). We discuss the framework using examples from a smart factory.
    Type: conference contribution
    Volume: 2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)
    DOI: 10.1109/EDOCW49879.2020.00016
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/111833
    Scopus© Citations 32