The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Colin J. Fidge - One of the best experts on this subject based on the ideXlab platform.
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workflow simulation for Operational Decision support
Data and Knowledge Engineering, 2009Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also use logged data describing the system's observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
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Workflow simulation for Operational Decision support
Data and Knowledge Engineering, 2009Co-Authors: A. Rozinat, Arthur H.m. Ter Hofstede, Wil M. P. Van Der Aalst, Moe Thandar Wynn, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also use logged data describing the system's observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM. © 2009 Elsevier B.V. All rights reserved.
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BPM - Workflow Simulation for Operational Decision Support Using Design, Historic and State Information
Lecture Notes in Computer Science, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision supportin the context of workflow management. To do this we exploit not only the workflow's design, but also logged data describing the system's observed historicbehavior, and information extracted about the current stateof the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
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workflow simulation for Operational Decision support using yawl and prom
BPM reports, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, Van Der Wmp Wil Aalst, Ter Ahm Arthur Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of work ow management. To do this we exploit not only the work ow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the work ow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for dierent scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM. This technical report contains a detailed description of how a simulation model including Operational Decision support can be generated by our software based on the running example.
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workflow simulation for Operational Decision support using design historic and state information
Lecture Notes in Computer Science, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM.
W M P Van Der Aalst - One of the best experts on this subject based on the ideXlab platform.
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FASE - An Operational Decision support framework for monitoring business constraints
Fundamental Approaches to Software Engineering, 2012Co-Authors: Fabrizio Maria Maggi, Marco Montali, W M P Van Der AalstAbstract:Only recently, process mining techniques emerged that can be used for Operational Decision Support (OS), i.e., knowledge extracted from event logs is used to handle running process instances better. In the process mining tool ProM, a generic OS service has been developed that allows ProM to dynamically interact with an external information system, receiving streams of events and returning meaningful insights on the running process instances. In this paper, we present the implementation of a novel business constraints monitoring framework on top of the ProM OS service. We discuss the foundations of the monitoring framework considering two logic-based approaches, tailored to Linear Temporal Logic on finite traces and the Event Calculus.
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an Operational Decision support framework for monitoring business constraints
Fundamental Approaches to Software Engineering, 2012Co-Authors: Fabrizio Maria Maggi, Marco Montali, W M P Van Der AalstAbstract:Only recently, process mining techniques emerged that can be used for Operational Decision Support (OS), i.e., knowledge extracted from event logs is used to handle running process instances better. In the process mining tool ProM, a generic OS service has been developed that allows ProM to dynamically interact with an external information system, receiving streams of events and returning meaningful insights on the running process instances. In this paper, we present the implementation of a novel business constraints monitoring framework on top of the ProM OS service. We discuss the foundations of the monitoring framework considering two logic-based approaches, tailored to Linear Temporal Logic on finite traces and the Event Calculus.
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workflow simulation for Operational Decision support
Data and Knowledge Engineering, 2009Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also use logged data describing the system's observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
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BPM - Workflow Simulation for Operational Decision Support Using Design, Historic and State Information
Lecture Notes in Computer Science, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision supportin the context of workflow management. To do this we exploit not only the workflow's design, but also logged data describing the system's observed historicbehavior, and information extracted about the current stateof the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
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workflow simulation for Operational Decision support using design historic and state information
Lecture Notes in Computer Science, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM.
Moe Thandar Wynn - One of the best experts on this subject based on the ideXlab platform.
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workflow simulation for Operational Decision support
Data and Knowledge Engineering, 2009Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also use logged data describing the system's observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
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Workflow simulation for Operational Decision support
Data and Knowledge Engineering, 2009Co-Authors: A. Rozinat, Arthur H.m. Ter Hofstede, Wil M. P. Van Der Aalst, Moe Thandar Wynn, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also use logged data describing the system's observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM. © 2009 Elsevier B.V. All rights reserved.
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BPM - Workflow Simulation for Operational Decision Support Using Design, Historic and State Information
Lecture Notes in Computer Science, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision supportin the context of workflow management. To do this we exploit not only the workflow's design, but also logged data describing the system's observed historicbehavior, and information extracted about the current stateof the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
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workflow simulation for Operational Decision support using yawl and prom
BPM reports, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, Van Der Wmp Wil Aalst, Ter Ahm Arthur Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of work ow management. To do this we exploit not only the work ow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the work ow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for dierent scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM. This technical report contains a detailed description of how a simulation model including Operational Decision support can be generated by our software based on the running example.
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workflow simulation for Operational Decision support using design historic and state information
Lecture Notes in Computer Science, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM.
A. Rozinat - One of the best experts on this subject based on the ideXlab platform.
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workflow simulation for Operational Decision support
Data and Knowledge Engineering, 2009Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also use logged data describing the system's observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
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Workflow simulation for Operational Decision support
Data and Knowledge Engineering, 2009Co-Authors: A. Rozinat, Arthur H.m. Ter Hofstede, Wil M. P. Van Der Aalst, Moe Thandar Wynn, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also use logged data describing the system's observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM. © 2009 Elsevier B.V. All rights reserved.
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BPM - Workflow Simulation for Operational Decision Support Using Design, Historic and State Information
Lecture Notes in Computer Science, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision supportin the context of workflow management. To do this we exploit not only the workflow's design, but also logged data describing the system's observed historicbehavior, and information extracted about the current stateof the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
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workflow simulation for Operational Decision support using yawl and prom
BPM reports, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, Van Der Wmp Wil Aalst, Ter Ahm Arthur Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of work ow management. To do this we exploit not only the work ow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the work ow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for dierent scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM. This technical report contains a detailed description of how a simulation model including Operational Decision support can be generated by our software based on the running example.
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workflow simulation for Operational Decision support using design historic and state information
Lecture Notes in Computer Science, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM.
A H M Ter Hofstede - One of the best experts on this subject based on the ideXlab platform.
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workflow simulation for Operational Decision support
Data and Knowledge Engineering, 2009Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also use logged data describing the system's observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
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BPM - Workflow Simulation for Operational Decision Support Using Design, Historic and State Information
Lecture Notes in Computer Science, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision supportin the context of workflow management. To do this we exploit not only the workflow's design, but also logged data describing the system's observed historicbehavior, and information extracted about the current stateof the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.
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workflow simulation for Operational Decision support using design historic and state information
Lecture Notes in Computer Science, 2008Co-Authors: A. Rozinat, Moe Thandar Wynn, W M P Van Der Aalst, A H M Ter Hofstede, Colin J. FidgeAbstract:Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for Operational Decision making and continuous improvement. Here we describe a simulation system for Operational Decision support in the context of workflow management. To do this we exploit not only the workflow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM.
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business process simulation for Operational Decision support
Business Process Management, 2007Co-Authors: Moe Thandar Wynn, Colin J. Fidge, A H M Ter Hofstede, Marlon Dumas, W M P Van Der AalstAbstract:Contemporary business process simulation environments are geared towards design-time analysis, rather than Operational Decision support over already deployed and running processes. In particular, simulation experiments in existing process simulation environments start from an empty execution state. We investigate the requirements for a process simulation environment that allows simulation experiments to start from an intermediate execution state. We propose an architecture addressing these requirements and demonstrate it through a case study conducted using the YAWL workflow engine and CPN simulation tools.
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Business Process Management Workshops - Business process simulation for Operational Decision support
Business Process Management Workshops, 2007Co-Authors: Moe Thandar Wynn, Colin J. Fidge, A H M Ter Hofstede, Marlon Dumas, W M P Van Der AalstAbstract:Contemporary business process simulation environments are geared towards design-time analysis, rather than Operational Decision support over already deployed and running processes. In particular, simulation experiments in existing process simulation environments start from an empty execution state. We investigate the requirements for a process simulation environment that allows simulation experiments to start from an intermediate execution state. We propose an architecture addressing these requirements and demonstrate it through a case study conducted using the YAWL workflow engine and CPN simulation tools.