Web Services for Blended Learning Patterns
ICALT '04 Proceedings of the IEEE International Conference on Advanced Learning Technologies
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Rediscovering workflow models from event-based data using little thumb
Integrated Computer-Aided Engineering
Design and verification of instantiable compliance rule graphs in process-aware information systems
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Simplifying mined process models: an approach based on unfoldings
BPM'11 Proceedings of the 9th international conference on Business process management
Process mining and verification of properties: an approach based on temporal logic
OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I
Assessing medical treatment compliance based on formal process modeling
USAB'11 Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health
Enhancing declare maps based on event correlations
BPM'13 Proceedings of the 11th international conference on Business Process Management
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Existing process mining approaches are able to tolerate a certain degree of noise in the process log. However, processes that contain infrequent paths, multiple (nested) parallel branches, or have been changed in an ad-hoc manner, still pose major challenges. For such cases, process mining typically returns "spaghetti-models", that are hardly usable even as a starting point for process (re-)design. In this paper, we address these challenges by introducing data transformation and pre-processing steps that improve and ensure the quality of mined models for existing process mining approaches. We propose the concept of semantic log purging, the cleaning of logs based on domain specific constraints utilizing semantic knowledge which typically complements processes. Furthermore we demonstrate the feasibility and effectiveness of the approach based on a case study in the higher education domain. We think that semantic log purging will enable process mining to yield better results, thus giving process (re-)designers a valuable tool.