Title data
Schönig, Stefan ; Günther, Christoph ; Jablonski, Stefan:
Process Discovery and Guidance Applications of Manually Generated Logs.
In:
International Academy, Research, and Industry Association - IARIA
(ed.):
The Seventh International Conference on Internet Monitoring and Protection. -
Wilmingston, DE
: IARIA
,
2012
. - pp. 61-67
ISBN 978-1-61208-201-1
Related URLs
Abstract in another language
In this paper, we investigate the problem of the availability of complete process execution event logs in order to offer automatic process model generation (process discovery) possibility by process mining techniques. Therefore, we present our AI4 | Process Observation Project that generates manual logs and guides process participants through process execution. Like this, our project offers the possibility for the automatic generation of process models within organizations, without the availability of any information system. Process participants are encouraged to work with our AI4 | Process Observation Tool by various process execution support functions, like an auto-suggestion of process data and dynamic recommendations of following processes.
Further data
Item Type: | Article in a book |
---|---|
Refereed: | Yes |
Keywords: | Process Mining; Process Monitoring; Activity Tracking; Guidance through Process Execution |
Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV > Chair Applied Computer Science IV - Univ.-Prof. Dr.-Ing. Stefan Jablonski Faculties Faculties > Faculty of Mathematics, Physics und Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV |
Result of work at the UBT: | Yes |
DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science |
Date Deposited: | 23 Oct 2017 12:22 |
Last Modified: | 02 Nov 2022 13:44 |
URI: | https://eref.uni-bayreuth.de/id/eprint/34896 |