Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Maximal Repetitions in a Word in Linear Time
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Constructing Suffix Tree for Gigabyte Sequences with Megabyte Memory
IEEE Transactions on Knowledge and Data Engineering
Linear time algorithms for finding and representing all the tandem repeats in a string
Journal of Computer and System Sciences
Tandem repeats over the edit distance
Bioinformatics
Mining taxonomies of process models
Data & Knowledge Engineering
Process Model Abstraction: A Slider Approach
EDOC '08 Proceedings of the 2008 12th International IEEE Enterprise Distributed Object Computing Conference
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
Mining hierarchies of models: from abstract views to concrete specifications
BPM'05 Proceedings of the 3rd international conference on Business Process Management
Trace alignment in process mining: opportunities for process diagnostics
BPM'10 Proceedings of the 8th international conference on Business process management
Handling concept drift in process mining
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
Process diagnostics using trace alignment: Opportunities, issues, and challenges
Information Systems
RACE: a scalable and elastic parallel system for discovering repeats in very long sequences
Proceedings of the VLDB Endowment
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Process mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate spaghetti-like process models that are hard to comprehend. One reason for such a result can be attributed to constructing process models from raw traces without due pre-processing. In an event log, there can be instances where the system is subjected to similar execution patterns/behavior. Discovery of common patterns of invocation of activities in traces (beyond the immediate succession relation) can help in improving the discovery of process models and can assist in defining the conceptual relationship between the tasks/activities. In this paper, we characterize and explore the manifestation of commonly used process model constructs in the event log and adopt pattern definitions that capture these manifestations, and propose a means to form abstractions over these patterns. We also propose an iterative method of transformation of traces which can be applied as a pre-processing step for most of today's process mining techniques . The proposed approaches are shown to identify promising patterns and conceptually-valid abstractions on a real-life log. The patterns discussed in this paper have multiple applications such as trace clustering, fault diagnosis/anomaly detection besides being an enabler for hierarchical process discovery.