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VESPA Visual Event-Stream Progressive Analytics Andrea Burattin, Silvia Miksch, Shazia Sadiq, Hans-Jörg Schulz and Katerina Vrotsou 2nd Visual Process Analytics Workshop (VIPRA) Co-located with BPM 2025

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Reference Scenario • Emergency room at a hospital • Patients are tracked using the Hospital Information System (HIS) • Different patient handling policies, based on conditions • Manager On Duty (MOD) monitors KPIs to deploy countermeasures 3 Bullfrog’s Theme Hospital, 1997

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Reference Scenario • User stories from our scenario • US1: Patient-centric As an ER MOD, I want to know if the LOS (length of stay) for one ER patient is too high, so that I can prioritize them in the waiting queue. • US2: Ward-centric As an ER MOD, I want to know if the overall LOS for a cohort of (or all) ER patients is too high or too low, so I can adjust the allocation of resources. 4 Bullfrog’s Theme Hospital, 1997

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Possible conceptual frameworks 5 Streaming Process Mining Inspect one event at a time and deliver immediately the (potentially partial) result. Burattin, A. (2022). Streaming Process Mining. In: van der Aalst, W.M.P., Carmona, J. (eds) Process Mining Handbook. Lecture Notes in Business Information Processing, vol 448. Springer, Cham. https://doi.org/10.1007/978-3-031-08848-3_11

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Possible conceptual frameworks (cont.) 6 Progressive VA Partial intermediate computational results and interactive visualizations. Angelini, M.; Santucci, G.; Schumann, H.; Schulz, H.-J. A Review and Characterization of Progressive Visual Analytics. Informatics 2018, 5, 31. https://doi.org/10.3390/informatics5030031

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User roles and tasks in PVA 7 Adaptation from Fekete, J.D., Fisher, D., Sedlmair, M. (eds.): Progressive Data Analysis – Roadmap and Research Agenda. Eurographics Press (2024). Image under CC-BY 4.0.

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Aspects of the problem space of VESPA • Context of the problem • Healthcare / ER • Data space • Data from HIS • Guards (attention triggers) • Conformance below threshold / Load of urgent cases/ LOS for each patient • Tasks • Discovery / Conformance checking / Statistics • Algorithm space • Streaming DFG discovery / Behavioral CC / Statistics • Visualizations • [see next slides] 8

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Research questions • What are the required time points for progressive visualization for streaming process mining? • Attention of the user neeed • Scheduled / triggered / on demand • What are the effective, efficient, and appropriate progressive visualizations and interactions for streaming process mining? • What is needed / avoid cognitive overload / cost-benefit for the task 9

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Users and tasks 10 Tasks US1: Patient-centric As an ER administrator, I want to know if the LOS (length of stay) for one ER patient is too high, so that I can prioritize them in the waiting queue. US2: Ward-centric As an ER administrator, I want to know if the overall LOS for a cohort of (or all) ER patients is too high or too low, so I can adjust the allocation of resources. Observer Explorer Spectrum of users In progressive VA Searcher

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VESPA’s architecture diagram 11

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Data generation • We generated a population of patients • We generated a synthetic event stream • Including behavior drifts 12 M1: Regular M2: Off-peak M1: Regular M3: Intense

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Streaming Process Mining • pyBeamline pipeline to fulfill the goal 13 https://beamline.cloud/pybeamline/

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VESPA-VIS mockup 14 US2: Ward-centric As an ER administrator, I want to know if the overall LOS for a cohort of (or all) ER patients is too high or too low, so I can adjust the allocation of resources.

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15 US1: Patient-centric As an ER administrator, I want to know if the LOS (length of stay) for one ER patient is too high, so that I can prioritize them in the waiting queue. VESPA-VIS mockup

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Closing remarks • Progressive VA and Streaming PM perfectly complement each other • We characterize possible problem dimensions when considering adopting PVA and SPM, grounded in a concrete use case • We defined a pipeline/architecture and 2 mockups • Configuration of the parameters (both for the mining and the visualization) represents the most pressing challenge 16