Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Model management 2.0: manipulating richer mappings
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Mining Process Variants: Goals and Issues
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 2
Discovering Reference Process Models by Mining Process Variants
ICWS '08 Proceedings of the 2008 IEEE International Conference on Web Services
On Measuring Process Model Similarity Based on High-Level Change Operations
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Outlier detection techniques for process mining applications
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Process discovery: capturing the invisible
IEEE Computational Intelligence Magazine
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Event cube: another perspective on business processes
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Process mining and petri net synthesis
BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
Mining staff assignment rules from event-based data
BPM'05 Proceedings of the Third international conference on Business Process Management
Genetic process mining: a basic approach and its challenges
BPM'05 Proceedings of the Third international conference on Business Process Management
| Hi-index | 0.00 |
Health services research (HSR) is a scientific field analyzing personal health services regarding quality and efficiency. In particular, it focuses on discovering and analyzing health care processes. Of special interest are the factors of influence like the age of the patients and their impact on the processes. Multidimensional process mining is a way to discover health care processes according to certain factors of influence. Because the existing approach for multidimensional process mining from literature is unsatisfactory (e.g., missing separation of process model and visualization), a novel approach is developed. The basic idea is to move the multidimensional aspects directly into the event log which is a record of all events of the process activities serving as a foundation for process mining. OLAP operations are defined to select traces as input for usual process discovery algorithms. For a representation of results adequate to the user needs, several kinds of visualization are provided. During an optional step of consolidation it is possible to reduce the complexity of the mining results, e.g., by clustering process models. This paper motivates this approach and outlines its basic ideas at the early stage of PhD work.