Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches
Information Systems Research
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs
Data Mining and Knowledge Discovery
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Business process mining: An industrial application
Information Systems
Mining process models with non-free-choice constructs
Data Mining and Knowledge Discovery
Efficient mining of frequent episodes from complex sequences
Information Systems
Exploiting Inductive Logic Programming Techniques for Declarative Process Mining
Transactions on Petri Nets and Other Models of Concurrency II
Robust Process Discovery with Artificial Negative Events
The Journal of Machine Learning Research
Top-down induction of first-order logical decision trees
Artificial Intelligence
Inducing declarative logic-based models from labeled traces
BPM'07 Proceedings of the 5th international conference on Business process management
Applying inductive logic programming to process mining
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
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Process mining is the research area that is concerned with knowledge discovery from event logs. Process mining faces notable difficulties. One is that process mining is commonly limited to the harder setting of unsupervised learning, since negative information about state transitions that were prevented from taking place (i.e. negative events) is often unavailable in real-life event logs. We propose a method to enhance process event logs with artificially generated negative events, striving towards the induction of a set of negative examples that is both correct (containing no false negative events) and complete (containing all, non-trivial negative events). Such generated sets of negative events can advantageously be applied for discovery and evaluation purposes, and in auditing and compliance settings.