Praktikum - Implementation of Process Mining Algorithms: Transformative Business Knowledge (IN0012, IN2106, IN2128, IN4304)
Practical course - Implementation of Process Mining Algorithms: Transformative Business Knowledge (IN0012, IN2106, IN2128, IN4304)

Lehrveranstaltung 0000005697 im SS 2021

Basisdaten

LV-Art

Praktikum

Umfang

6 SWS

betreuende Organisation

Informatik 17 - Lehrstuhl für Wirtschaftsinformatik und Geschäftsprozessmanagement (Prof. Rinderle-Ma)

Dozent(inn)en

Karolin Winter

Termine

6 einzelne oder verschobene Termine

iCalendar

	Datum und Zeit	Raum und Anmerkung
!	Di, 06.07.2021, 15:00–17:00	Introduction to Topic
!	Mi, 14.07.2021, 15:00–17:00	Evaluation of schedule and proposed methods
!	Di, 10.08.2021, 15:00–17:00	Intermediate evaluation
!	Di, 24.08.2021, 15:00–17:00	Intermediate evaluation
!	Di, 14.09.2021, 15:00–17:00	Preparation for presentation
!	Di, 21.09.2021, 14:00–17:00	Presentation

Zuordnung zu Modulen

IN2106: Master-Praktikum / Advanced Practical Course
Dieses Modul ist in den folgenden Katalogen enthalten:
- weitere Module aus anderen Fachrichtungen

weitere Informationen

Lehrveranstaltungen sind neben Prüfungen Bausteine von Modulen. Beachten Sie daher, dass Sie Informationen zu den Lehrinhalten und insbesondere zu Prüfungs- und Studienleistungen in der Regel nur auf Modulebene erhalten können (siehe Abschnitt "Zuordnung zu Modulen" oben).

ergänzende Hinweise	Process discovery (cf. [1] for an introduction on the topic) provides a valuable means for companies to analyze process execution data. Tools like ProM (https://www.promtools.org/) or libraries like PM4Py (https://pm4py.fit.fraunhofer.de/) offer a wide range of process discovery algorithms but have to be either installed locally or are not straight-forward to use for non-technical experts. Therefore, within this practical course students will implement a webservice to provide process discovery results in a light-weight form in order to facilitate the analysis of process execution data for non-technical experts. The webservice should take a XES file as input and depict the results of a process discovery algorithm which is to be implemented by the students as well. For both, the webservice as well as the implementation of the discovery algorithm, the choice of the programming language is up to the students. [1] Wil Van Der Aalst. Process mining: data science in action, volume 2. Springer, 2016. doi: 10.1007/978-3-662-49851-4. URL: https://doi.org/10.1007/978-3-662-49851-4.
Links	TUMonline-Eintrag

ergänzende Hinweise

Process discovery (cf. [1] for an introduction on the topic) provides a valuable means for companies to analyze process execution data. Tools like ProM (https://www.promtools.org/) or libraries like PM4Py (https://pm4py.fit.fraunhofer.de/) offer a wide range of process discovery algorithms but have to be either installed locally or are not straight-forward to use for non-technical experts. Therefore, within this practical course students will implement a webservice to provide process discovery results in a light-weight form in order to facilitate the analysis of process execution data for non-technical experts. The webservice should take a XES file as input and depict the results of a process discovery algorithm which is to be implemented by the students as well. For both, the webservice as well as the implementation of the discovery algorithm, the choice of the programming language is up to the students. [1] Wil Van Der Aalst. Process mining: data science in action, volume 2. Springer, 2016. doi: 10.1007/978-3-662-49851-4. URL: https://doi.org/10.1007/978-3-662-49851-4.

Links

TUMonline-Eintrag

Gleiche Lehrveranstaltungen (z. B. in anderen Semestern)

Semester	Titel	Dozent(en)	Termine
SS 2022	Praktikum - Implementation of Process Mining Algorithms: Transformative Business Knowledge (IN0012, IN2106, IN2130, IN4304)	Winter, K. Leitung/Koordination: Rinderle-Ma, S.	einzelne oder verschobene Termine
WS 2021/2	Bachelor-/Master-Praktikum - Implementation of Process Mining Algorithms: Transformative Business Knowledge (IN0012, IN2130, IN2106, IN4304)	Winter, K. Leitung/Koordination: Rinderle-Ma, S.	einzelne oder verschobene Termine