Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An overview of workflow management: from process modeling to workflow automation infrastructure
Distributed and Parallel Databases - Special issue on software support for work flow management
Automating process discovery through event-data analysis
Proceedings of the 17th international conference on Software engineering
ACM SIGMOD Record
Logic based modeling and analysis of workflows
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Complexity of graph partition problems
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Workflow management: models, methods, and systems
Workflow management: models, methods, and systems
Workflow management with service quality guarantees
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Failure Handling and Coordinated Execution of Concurrent Workflows
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Business Process Coordination: State of the Art, Trends, and Open Issues
Proceedings of the 27th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A Formal Foundation for Distributed Workflow Execution Based on State Charts
ICDT '97 Proceedings of the 6th International Conference on Database Theory
The VLDB Journal — The International Journal on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The social network of Java classes
Proceedings of the 2006 ACM symposium on Applied computing
Improving process models by discovering decision points
Information Systems
Discovering Structured Event Logs from Unstructured Audit Trails for Workflow Mining
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Mining hierarchies of models: from abstract views to concrete specifications
BPM'05 Proceedings of the 3rd international conference on Business Process Management
Process management in health care: a system for preventing risks and medical errors
BPM'05 Proceedings of the 3rd international conference on Business Process Management
Mining constrained graphs: the case of workflow systems
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
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A workflow is a partial or total automation of a business process, in which a collection of activities must be executed by humans or machines, according to certain procedural rules. This paper deals with an aspect of workflows which has not so far received much attention: providing facilities for the human system administrator to monitor the actual behavior of the workflow system in order to predict the "most probable" workflow executions. In this context, we develop a data mining algorithm for identifying frequent patterns, i.e., the workflow substructures that have been scheduled more frequently by the system. Several experiments show that our algorithm outperforms the standard approaches adapted to mining frequent instances.