Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
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Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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Proceedings of the 27th International Conference on Very Large Data Bases
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EDCIS '02 Proceedings of the First International Conference on Engineering and Deployment of Cooperative Information Systems
Logging and Post-Mortem Analysis of Workflow Executions Based on Event Histories
RIDS '97 Proceedings of the Third International Workshop on Rules in Database Systems
DEXA '98 Proceedings of the 9th International Workshop on Database and Expert Systems Applications
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HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
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RIDE '99 Proceedings of the Ninth International Workshop on Research Issues on Data Engineering: Information Technology for Virtual Enterprises
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ICDE '00 Proceedings of the 16th International Conference on Data Engineering
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IEEE Transactions on Knowledge and Data Engineering
Pattern Classification (2nd Edition)
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Web Services: Concepts, Architectures and Applications
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SENTINEL: a semantic business process monitoring tool
OBI '08 Proceedings of the first international workshop on Ontology-supported business intelligence
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ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
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EDOC'09 Proceedings of the 13th IEEE international conference on Enterprise Distributed Object Computing
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BIRTE'06 Proceedings of the 1st international conference on Business intelligence for the real-time enterprises
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OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems - Volume Part II
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ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
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ICSOC/ServiceWave'09 Proceedings of the 2009 international conference on Service-oriented computing
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ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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BPM'06 Proceedings of the 2006 international conference on Business Process Management Workshops
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Applied Soft Computing
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DESRIST'13 Proceedings of the 8th international conference on Design Science at the Intersection of Physical and Virtual Design
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Distributed and Parallel Databases
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Business process management tools have traditionally focused on supporting the modeling and automation of business processes, with the aim of enabling faster and more cost-effective process executions. As more and more processes become automated, customers become increasingly interested in managing process executions. Specifically, there is a desire for getting more visibility into process executions, to be able to quickly spot problems and areas for improvements. The idea is that, by being able to assess the process execution quality, it is possible to take actions to improve and optimize process execution, thereby leading to processes that have higher quality and lower costs. All this is possible today, but involves the execution of specialized data mining projects that typically last months, costs hundreds of thousands of dollars, and only provide a specialized, narrow solution whose applicability is often relatively short in time, due to the ever changing business and IT environments. Still, the need is such that companies undertake these efforts.To address these needs, this paper presents a set of concepts and architectures that lay the foundation for providing users with intelligent analysis and predictions about business process executions. For example, the tools are able to provide users with information about why the quality of a process execution is low, what will be the outcome of a certain process, or how many processes will be started next week. This information is crucial to gain visibility into the processes, understand or foresee problems and areas of optimization, and quickly identify solutions. Intelligent analysis and predictions are achieved by applying data mining techniques to process execution data. In contrast to traditional approaches, where lengthy projects, considerable efforts, and specialized skills in both business processes and data mining are needed to achieve these objectives, we aim at automating the entire data mining process lifecycle, so that intelligent functionality can be provided by the system while requiring little or no user input. The ambitious end goal of the work presented in this paper is that of laying the foundation for a framework and tool that is capable of providing analysts with key intelligence information about process execution, affecting crucial IT and business decisions, almost literally at the click of a button.