Algorithms for clustering data
Algorithms for clustering data
The multiple sequence alignment problem in biology
SIAM Journal on Applied Mathematics
Distributed and Parallel Databases
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Conformance checking of processes based on monitoring real behavior
Information Systems
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Abstractions in Process Mining: A Taxonomy of Patterns
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Outlier detection techniques for process mining applications
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Trace alignment in process mining: opportunities for process diagnostics
BPM'10 Proceedings of the 8th international conference on Business process management
Data & Knowledge Engineering
Repairing process models to reflect reality
BPM'12 Proceedings of the 10th international conference on Business Process Management
Bridging abstraction layers in process mining by automated matching of events and activities
BPM'13 Proceedings of the 11th international conference on Business Process Management
BPM'13 Proceedings of the 11th international conference on Business Process Management
Monitoring care processes in the gynecologic oncology department
Computers in Biology and Medicine
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Business processes leave trails in a variety of data sources (e.g., audit trails, databases, and transaction logs). Hence, every process instance can be described by a trace, i.e., a sequence of events. Process mining techniques are able to extract knowledge from such traces and provide a welcome extension to the repertoire of business process analysis techniques. Recently, process mining techniques have been adopted in various commercial BPM systems (e.g., BPM|one, Futura Reflect, ARIS PPM, Fujitsu Interstage, Businesscape, Iontas PDF, and QPR PA). Unfortunately, traditional process discovery algorithms have problems dealing with less structured processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in such a way that event logs can be explored easily. Trace alignment can be used to explore the process in the early stages of analysis and to answer specific questions in later stages of analysis. Hence, it complements existing process mining techniques focusing on discovery and conformance checking. The proposed techniques have been implemented as plugins in the ProM framework. We report the results of trace alignment on one synthetic and two real-life event logs, and show that trace alignment has significant promise in process diagnostic efforts.