Simulation: a statistical perspective
Simulation: a statistical perspective
Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
A Course in Simulation
A Machine Learning Approach to Workflow Management
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Time Constraints in Workflow Systems
CAiSE '99 Proceedings of the 11th International Conference on Advanced Information Systems Engineering
Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches
Information Systems Research
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Process Aware Information Systems: Bridging People and Software Through Process Technology
Process Aware Information Systems: Bridging People and Software Through Process Technology
Probabilistic Calculation of Execution Intervals for Workflows
TIME '05 Proceedings of the 12th International Symposium on Temporal Representation and Reasoning
Business process mining: An industrial application
Information Systems
Coloured Petri Nets and CPN Tools for modelling and validation of concurrent systems
International Journal on Software Tools for Technology Transfer (STTT)
Rediscovering workflow models from event-based data using little thumb
Integrated Computer-Aided Engineering
Conformance checking of processes based on monitoring real behavior
Information Systems
DECLARE: Full Support for Loosely-Structured Processes
EDOC '07 Proceedings of the 11th IEEE International Enterprise Distributed Object Computing Conference
Discovering colored Petri nets from event logs
International Journal on Software Tools for Technology Transfer (STTT)
Process Discovery Using Integer Linear Programming
PETRI NETS '08 Proceedings of the 29th international conference on Applications and Theory of Petri Nets
Supporting Flexible Processes through Recommendations Based on History
BPM '08 Proceedings of the 6th International Conference on Business Process Management
Workflow Simulation for Operational Decision Support Using Design, Historic and State Information
BPM '08 Proceedings of the 6th International Conference on Business Process Management
Cycle Time Prediction: When Will This Case Finally Be Finished?
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
Information Systems
ProM 4.0: comprehensive support for real process analysis
ICATPN'07 Proceedings of the 28th international conference on Applications and theory of Petri nets and other models of concurrency
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
Beyond process mining: from the past to present and future
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
History-Dependent stochastic petri nets
PSI'09 Proceedings of the 7th international Andrei Ershov Memorial conference on Perspectives of Systems Informatics
Real-time process data acquisition with Bluetooth
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Causal relation detection for activities from heterogeneous sources
ICWE'11 Proceedings of the 11th international conference on Current Trends in Web Engineering
Developing a real-time process data acquisition system for automatic process measurement
GPC'11 Proceedings of the 6th international conference on Grid and Pervasive Computing
Process Mining: Overview and Opportunities
ACM Transactions on Management Information Systems (TMIS)
Replaying history on process models for conformance checking and performance analysis
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
PETRI NETS'12 Proceedings of the 33rd international conference on Application and Theory of Petri Nets
Repairing process models to reflect reality
BPM'12 Proceedings of the 10th international conference on Business Process Management
Context-Aware predictions on business processes: an ensemble-based solution
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Generating multi-objective optimized business process enactment plans
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
Supporting risk-informed decisions during business process execution
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
Profiling event logs to configure risk indicators for process delays
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
preCEP: facilitating predictive event-driven process analytics
DESRIST'13 Proceedings of the 8th international conference on Design Science at the Intersection of Physical and Virtual Design
Real-time risk monitoring in business processes: A sensor-based approach
Journal of Systems and Software
Business process mining from e-commerce web logs
BPM'13 Proceedings of the 11th international conference on Business Process Management
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Process mining allows for the automated discovery of process models from event logs. These models provide insights and enable various types of model-based analysis. This paper demonstrates that the discovered process models can be extended with information to predict the completion time of running instances. There are many scenarios where it is useful to have reliable time predictions. For example, when a customer phones her insurance company for information about her insurance claim, she can be given an estimate for the remaining processing time. In order to do this, we provide a configurable approach to construct a process model, augment this model with time information learned from earlier instances, and use this to predict e.g., the completion time. To provide meaningful time predictions we use a configurable set of abstractions that allow for a good balance between ''overfitting'' and ''underfitting''. The approach has been implemented in ProM and through several experiments using real-life event logs we demonstrate its applicability.