Francesca Zerbato
Title
Ph.D.
Last Name
Zerbato
First name
Francesca
Email
francesca.zerbato@unisg.ch
Phone
+41 71 224 26 69
4 results
Now showing 1 - 4 of 4
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Item type:Publication, Exploring how users engage with hybrid process artifacts based on declarative process models: a behavioral analysis based on eye-tracking and think-aloud(Springer Science and Business Media LLC, 2020-07-05); ; ;Burattin, Andrea ;Slaats, TijsThomas T. HildebrandtType:journal articleJournal:Software and Systems ModelingVolume:20Issue:5Scopus© Citations 24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Framework to Support the Validation of Process Mining Inquiries(2024); ; In exploratory process mining, analysts often start with limited knowledge of the log. As they seek to improve their understanding of the log, they develop expectations about what the results might be. Based on these expectations, they then make inquiries and translate them into queries against the log. However, during the analysis, analysts need to evaluate and compare the results of their queries to be able to validate them against their expectations. In this paper, we propose a framework to support process analysts in validating their query results and to enable them to reflect on their analytical process. The framework helps analysts to record their queries and results and allows them to characterize and compare the results obtained with different queries, thereby facilitating the validation process. We implemented the framework as a Python library that can be easily extended and integrated into existing process mining environments. We also demonstrated the usefulness of the framework through an extensive analysis of a real event log.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards IoT-Driven Process Event Log Generation for Conformance Checking in Smart Factories(2020-09); ; ;Burattin, Andrea ;García-Bañuelos, LucianoThe 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 contributionVolume:2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)Scopus© Citations 52 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Fresh Approach to Analyze Process Outcomes(2023-10-23); ; ;Timothy SulzerType:Controlled Vocabulary for Resource Type Genres::text::conference objectJournal:2023 5th International Conference on Process Mining (ICPM)