Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Mining taxonomies of process models
Data & Knowledge Engineering
The refined process structure tree
Data & Knowledge Engineering
Process Discovery using Integer Linear Programming
Fundamenta Informaticae - Petri Nets 2008
What makes process models understandable?
BPM'07 Proceedings of the 5th international conference on Business process management
Process mining based on clustering: a quest for precision
BPM'07 Proceedings of the 2007 international conference on Business process management
Similarity of business process models: Metrics and evaluation
Information Systems
APROMORE: An advanced process model repository
Expert Systems with Applications: An International Journal
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Thresholds for error probability measures of business process models
Journal of Systems and Software
A Study Into the Factors That Influence the Understandability of Business Process Models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Approximate clone detection in repositories of business process models
BPM'12 Proceedings of the 10th international conference on Business Process Management
Business Process Model Merging: An Approach to Business Process Consolidation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Fast detection of exact clones in business process model repositories
Information Systems
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Automated process discovery techniques aim at extracting models from information system logs in order to shed light into the business processes supported by these systems. Existing techniques in this space are effective when applied to relatively small or regular logs, but otherwise generate large and spaghetti-like models. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. The result is a collection of process models --- each one representing a variant of the business process --- as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically by means of subprocess extraction. The proposed technique allows users to set a desired bound for the complexity of the produced models. Experiments on real-life logs show that the technique produces collections of models that are up to 64% smaller than those extracted under the same complexity bounds by applying existing trace clustering techniques.