Online or onsite, instructor-led live Automated Business Process Discovery (ABPD, or Process Mining) training courses demonstrate through discussion and hands-on practice an overview of the most commonly used techniques for process mining, and the various process discovery algorithms and tools used for discovering and modeling processes based on raw event data. Real-life case studies are examined and data sets are analyzed using the ProM open-source framework.
ABPD training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Automated Business Process Discovery (ABPD) trainings in Innsbruck can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
NobleProg Innsbruck
Valiergasse 58, Innsbruck, Austria, 6020
Overview
Our training facilities are located at Valiergasse 58 in Innsbruck and offer optimal training conditions for your needs.
Directions
The NobleProg training facilities are conveniently located near the main train station, the A12 and A13 motorways are easily accessible.
Parking spaces
There are parking spaces in the surrounding streets around our training rooms.
Local infrastructure
There are numerous restaurants in the downtown area and hotels are also within walking distance.
Process mining, or Automated Business Process Discovery (ABPD), is a technique that applies algorithms to event logs for the purpose of analyzing business processes. Process mining goes beyond data storage and data analysis; it bridges data with processes and provides insights into the trends and patterns that affect process efficiency.
Format of the Course
The course starts with an overview of the most commonly used techniques for process mining. We discuss the various process discovery algorithms and tools used for discovering and modeling processes based on raw event data. Real-life case studies are examined and data sets are analyzed using the ProM open-source framework.
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Testimonials (1)
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Tomek Danowski - GE Medical Systems Polska Sp. Z O.O.
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