Applied multivariate statistical analysis
Applied multivariate statistical analysis
Introduction to algorithms
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Process innovation: reengineering work through information technology
Process innovation: reengineering work through information technology
Prototyping a Process Monitoring Experiment
IEEE Transactions on Software Engineering
Automating process discovery through event-data analysis
Proceedings of the 17th international conference on Software engineering
Modeling and Analysis of Workflows Using Petri Nets
Journal of Intelligent Information Systems - Special issue on workflow management systems
Machine Learning
Machine Learning
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Specifying and Enforcing Intertask Dependencies
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
A Formal Foundation for Distributed Workflow Execution Based on State Charts
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches
Information Systems Research
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
Discovery of temporal patterns from process instances
Computers in Industry - Special issue: Process/workflow mining
Learning inexpensive parametric design models using an augmented genetic programming technique
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Analysis of organizational dependency for urbanism of information systems
International Journal of Computer Integrated Manufacturing
Towards comprehensive support for organizational mining
Decision Support Systems
Classification and evaluation of timed running schemas for workflow based on process mining
Journal of Systems and Software
Temporal mining for interactive workflow data analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering expressive process models from noised log data
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Policy-Driven Process Mapping (PDPM): Discovering process models from business policies
Decision Support Systems
Supporting process design for e-business via an integrated process repository
Information Technology and Management
A process mining based approach to knowledge maintenance
Information Systems Frontiers
An information-theoretic framework for process structure and data mining
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Cross-organizational collaborative workflow mining from a multi-source log
Decision Support Systems
Comprehensive rule-based compliance checking and risk management with process mining
Decision Support Systems
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A thorough understanding of the way in which existing business processes currently practice is essential from the perspectives of both process reengineering and workflow management. In this paper, we present a framework and algorithms that derive the underlying process model from past executions. The process model employs a directed graph for representing the control dependencies among activities and associates a Boolean function on each edge to indicate the condition under which the edge is to be enabled. By modeling the execution of an activity as an interval, we have developed an algorithm that derives the directed graph in a faster, more accurate manner. This algorithm is further enhanced with a noise handling mechanism to tolerate noise, which frequently occur in the real world. Experimental results show that the proposed algorithm outperforms the existing ones in terms of efficiency and quality.