Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
Efficient Detection of Vacuity in Temporal Model Checking
Formal Methods in System Design - Special issue on CAV '97
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
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Business process mining: An industrial application
Information Systems
DECLARE: Full Support for Loosely-Structured Processes
EDOC '07 Proceedings of the 11th IEEE International Enterprise Distributed Object Computing Conference
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
Inducing declarative logic-based models from labeled traces
BPM'07 Proceedings of the 5th international conference on Business process management
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Automatic verification of data-centric business processes
BPM'11 Proceedings of the 9th international conference on Business process management
Process mining and verification of properties: an approach based on temporal logic
OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I
Patterns for a log-based strengthening of declarative compliance models
IFM'12 Proceedings of the 9th international conference on Integrated Formal Methods
Aligning event logs and declarative process models for conformance checking
BPM'12 Proceedings of the 10th international conference on Business Process Management
A knowledge-based integrated approach for discovering and repairing declare maps
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
Discovering data-aware declarative process models from event logs
BPM'13 Proceedings of the 11th international conference on Business Process Management
Enhancing declare maps based on event correlations
BPM'13 Proceedings of the 11th international conference on Business Process Management
Declarative modeling: an academic dream or the future for BPM?
BPM'13 Proceedings of the 11th international conference on Business Process Management
Monitoring business constraints with the event calculus
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Process mining techniques often reveal that real-life processes are more variable than anticipated. Although declarative process models are more suitable for less structured processes, most discovery techniques generate conventional procedural models. In this paper, we focus on discovering Declare models based on event logs. A Declare model is composed of temporal constraints. Despite the suitability of declarative process models for less structured processes, their discovery is far from trivial. Even for smaller processes there are many potential constraints. Moreover, there may be many constraints that are trivially true and that do not characterize the process well. Naively checking all possible constraints is computationally intractable and may lead to models with an excessive number of constraints. Therefore, we have developed an Apriori algorithm to reduce the search space. Moreover, we use new metrics to prune the model. As a result, we can quickly generate understandable Declare models for real-life event logs.