Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
Deriving Petri Nets from Finite Transition Systems
IEEE Transactions on Computers
Principles of data mining
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
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
Discovering Expressive Process Models by Clustering Log Traces
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
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
Towards comprehensive support for organizational mining
Decision Support Systems
Process Discovery using Integer Linear Programming
Fundamenta Informaticae - Petri Nets 2008
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
Process mining based on regions of languages
BPM'07 Proceedings of the 5th international conference on Business process management
Time prediction based on process mining
Information Systems
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
Modeling Business Processes: A Petri Net-Oriented Approach
Modeling Business Processes: A Petri Net-Oriented Approach
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
BPM'06 Proceedings of the 4th international conference on Business Process Management
Process mining from a basis of state regions
PETRI NETS'10 Proceedings of the 31st international conference on Applications and Theory of Petri Nets
Replaying history on process models for conformance checking and performance analysis
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
The Impact of SOA Implementation on IT-Business Alignment: A System Dynamics Approach
ACM Transactions on Management Information Systems (TMIS)
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Over the last decade, process mining emerged as a new research field that focuses on the analysis of processes using event data. Classical data mining techniques such as classification, clustering, regression, association rule learning, and sequence/episode mining do not focus on business process models and are often only used to analyze a specific step in the overall process. Process mining focuses on end-to-end processes and is possible because of the growing availability of event data and new process discovery and conformance checking techniques. Process models are used for analysis (e.g., simulation and verification) and enactment by BPM/WFM systems. Previously, process models were typically made by hand without using event data. However, activities executed by people, machines, and software leave trails in so-called event logs. Process mining techniques use such logs to discover, analyze, and improve business processes. Recently, the Task Force on Process Mining released the Process Mining Manifesto. This manifesto is supported by 53 organizations and 77 process mining experts contributed to it. The active involvement of end-users, tool vendors, consultants, analysts, and researchers illustrates the growing significance of process mining as a bridge between data mining and business process modeling. The practical relevance of process mining and the interesting scientific challenges make process mining one of the “hot” topics in Business Process Management (BPM). This article introduces process mining as a new research field and summarizes the guiding principles and challenges described in the manifesto.