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DTSTART:20251026T030000
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UID:calendar.30031.field_data.0@diag.uniroma1.it
DTSTAMP:20260406T003958Z
CREATED:20251030T201136Z
DESCRIPTION:Title: Process Mining on Distributed Data SourcesAbstract: Rece
 nt years have seen an immense uptake of the Internet-of-Things and applica
 tions that build on sensor data. Such applications have emerged in various
  domains. In logistics\, services use sea ship transponder data for monito
 ring the movement\, loading and unloading of vessels. In healthcare\, hosp
 itals install Real-Time Locating Systems to track events relating to staff
 \, patients\, and equipment in clinical pathways. Technically\, the mentio
 ned scenarios have in common that they support complex processes in a dist
 ributed environment with a distributed infrastructure that collects sensor
  data for managing a complex system. This means that the sensor data in th
 e above scenarios integrates into larger business processes.  To efficient
 ly address these scenarios with process mining requires novel process mini
 ng techniques for distributed event data. This talk will present applicati
 ons for distributed process mining as well as new techniques to address th
 e field.Bio: Agnes Koschmider is a professor of Business Informatics at th
 e University of Bayreuth. Prior to this position Agnes Koschmider was prof
 essor of Business Informatics at the Computer Science Institute of the Uni
 versity of Kiel. She completed her PhD and her habilitation in Applied Inf
 ormatics at KIT. Agnes conducts research on methods for data-driven analys
 is and explanation of processes using artificial intelligence. Her work ce
 nters on process analytics\, in particular the development of pipelines th
 at efficiently handle the entire chain from raw data (time series\, sensor
  events\, and video data) to process discovery.  Such data pipelines have 
 applications across a wide range of disciplines.
DTSTART;TZID=Europe/Paris:20251104T150000
DTEND;TZID=Europe/Paris:20251104T150000
LAST-MODIFIED:20251030T215410Z
LOCATION:Aula Magna\, DIAG
SUMMARY:Process Mining on Distributed Data Sources - Agnes Koschmider
URL;TYPE=URI:http://diag.uniroma1.it/node/30031
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