Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
Parallel Mining of Association Rules
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
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
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
Business Process Management: Concepts, Languages, Architectures
Business Process Management: Concepts, Languages, Architectures
A Region-Based Algorithm for Discovering Petri Nets from Event Logs
BPM '08 Proceedings of the 6th International Conference on Business Process Management
Robust Process Discovery with Artificial Negative Events
The Journal of Machine Learning Research
Declarative specification and verification of service choreographiess
ACM Transactions on the Web (TWEB)
Process Discovery using Integer Linear Programming
Fundamenta Informaticae - Petri Nets 2008
A fresh look at precision in process conformance
BPM'10 Proceedings of the 8th international conference on Business process management
Soundness of workflow nets: classification, decidability, and analysis
Formal Aspects of Computing
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Handling concept drift in process mining
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
Conformance Checking Using Cost-Based Fitness Analysis
EDOC '11 Proceedings of the 2011 IEEE 15th International Enterprise Distributed Object Computing Conference
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
Process mining from a basis of state regions
PETRI NETS'10 Proceedings of the 31st international conference on Applications and Theory of Petri Nets
Distributed data mining on grids: services, tools, and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Replaying history on process models for conformance checking and performance analysis
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Conformance checking in the large: partitioning and topology
BPM'13 Proceedings of the 11th international conference on Business Process Management
Decomposing Petri nets for process mining: A generic approach
Distributed and Parallel Databases
Process Discovery and Conformance Checking Using Passages
Fundamenta Informaticae - Application and Theory of Petri Nets and Concurrency, 2012
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Process mining techniques have matured over the last decade and more and more organization started to use this new technology. The two most important types of process mining are process discovery (i.e., learning a process model from example behavior recorded in an event log) and conformance checking (i.e., comparing modeled behavior with observed behavior). Process mining is motivated by the availability of event data. However, as event logs become larger (say terabytes), performance becomes a concern. The only way to handle larger applications while ensuring acceptable response times, is to distribute analysis over a network of computers (e.g., multicore systems, grids, and clouds). This paper provides an overview of the different ways in which process mining problems can be distributed. We identify three types of distribution: replication, a horizontal partitioning of the event log, and a vertical partitioning of the event log. These types are discussed in the context of both procedural (e.g., Petri nets) and declarative process models. Most challenging is the horizontal partitioning of event logs in the context of procedural models. Therefore, a new approach to decompose Petri nets and associated event logs is presented. This approach illustrates that process mining problems can be distributed in various ways.