Segmentation-based modeling for advanced targeted marketing
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Improving Business Process Quality through Exception Understanding, Prediction, and Prevention
Proceedings of the 27th International Conference on Very Large Data Bases
Computers in Industry - Special issue: Process/workflow mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Model-Driven Dashboards for Business Performance Reporting
EDOC '06 Proceedings of the 10th IEEE International Enterprise Distributed Object Computing Conference
Seeing is believing: designing visualizations for managing risk and compliance
IBM Systems Journal
A static compliance-checking framework for business process models
IBM Systems Journal
Compliance Flow - Managing the compliance of dynamic and complex processes
Knowledge-Based Systems
Decision Trees for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Business Compliance Governance in Service-Oriented Architectures
AINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications
Checking the Compliance of Timing Constraints in Software Applications
KSE '09 Proceedings of the 2009 International Conference on Knowledge and Systems Engineering
Compliance aware business process design
BPM'07 Proceedings of the 2007 international conference on Business process management
Toward Uncertain Business Intelligence: The Case of Key Indicators
IEEE Internet Computing
On the design of compliance governance dashboards for effective compliance and audit management
ICSOC/ServiceWave'09 Proceedings of the 2009 international conference on Service-oriented computing
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Automatically monitoring and enforcing compliance of service-based business processes with laws, regulations, standards, contracts, or policies is a hot issue in both industry and research. Little attention has however been paid to the problem of understanding non-compliance and improving business practices to prevent non-compliance in the future, a task that typically still requires human interpretation and intervention. Building upon work on automated detection of non-compliant situations, in this paper we propose a technique for the root-cause analysis of encountered problems and for the prediction of likely compliance states of running processes that leverages (i) on event-based service infrastructures, in order to collect execution evidence, and (ii) on the concept of key compliance indicator, in order to focus the analysis on the right data. We validate our ideas and algorithms on real data from an internal process of a hospital.