Topic
Process modeling
About: Process modeling is a research topic. Over the lifetime, 11639 publications have been published within this topic receiving 223996 citations. The topic is also known as: process simulation.
Papers published on a yearly basis
Papers
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01 Mar 2014TL;DR: The paper develops a semi-automated approach that improves the business performance of processes by deriving decision criteria from the experience gained through past process executions, which is evaluated using a simulation of a manufacturing process.
Abstract: Business processes entail a large number of decisions that affect their business performance. The criteria used in these decisions are not always formally specified and optimized. The paper develops a semi-automated approach that improves the business performance of processes by deriving decision criteria from the experience gained through past process executions. The premise that drives the approach is that it is possible to identify a process path that would yield best performance at a given context. The approach uses data mining techniques to identify the relationships between context, path decisions, and process outcomes, and derives decision rules from these relationships. It is evaluated using a simulation of a manufacturing process, whose results demonstrate the potential of improving the business performance through the rules generated by the approach.
98 citations
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01 Jan 2002TL;DR: A dynamic workflow model and a dynamic workflow management system for modeling and controlling the execution of inter-organizational business processes are described.
Abstract: In the competitive global marketplace, business organizations often need to team up and operate as a virtual enterprise to achieve common business goals. Since the business environment of a virtual enterprise is highly dynamic, it is necessary to develop a workflow technology that is capable of handling dynamic workflows across enterprise boundaries. The paper describes a dynamic workflow model and a dynamic workflow management system for modeling and controlling the execution of inter-organizational business processes. The model extends the underlying model of WfMC's WPDL by adding connectors, events, triggers and rules as its modeling constructs, encapsulating activity definitions, and allowing e-service requests as a part of the activity specification. The workflow management system makes use of an event and rule server to trigger business rules during the enactment of workflow processes to enforce business constraints and policies and/or to modify the process model at run-time. It also provides a mechanism to dynamically bind e-service requests to e-services.
98 citations
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TL;DR: This work proposes a method that constructs the process model from process log data, by determining the relations between process tasks, by employing machine learning technique to induce rule sets.
Abstract: Effective information systems require the existence of explicit process models. A completely specified process design needs to be developed in order to enact a given business process. This development is time consuming and often subjective and incomplete. We propose a method that constructs the process model from process log data, by determining the relations between process tasks. To predict these relations, we employ machine learning technique to induce rule sets. These rule sets are induced from simulated process log data generated by varying process characteristics such as noise and log size. Tests reveal that the induced rule sets have a high predictive accuracy on new data. The effects of noise and imbalance of execution priorities during the discovery of the relations between process tasks are also discussed. Knowing the causal, exclusive, and parallel relations, a process model expressed in the Petri net formalism can be built. We illustrate our approach with real world data in a case study.
98 citations
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TL;DR: A new technique for neural-network-based modeling of chemical processes is proposed, inspired by the technique of stacked generalization proposed by Wolpert, and results obtained demonstrate the promise of this approach for improved neural- network-based plant-process modeling.
Abstract: A new technique for neural-network-based modeling of chemical processes is proposed. Stacked neural networks allow multiple neural networks to be selected and used to model a given process. The idea is that improved predictions can be obtained using multiple networks, instead of simply selecting a single, hopefully optimal network, as is usually done. A methodology for stacking neural networks for plant-process modeling has been developed. This method is inspired by the technique of stacked generalization proposed by Wolpert. The proposed method has been applied and evaluated for three example problems, including the dynamic modeling of a nonlinear chemical process. Results obtained demonstrate the promise of this approach for improved neural-network-based plant-process modeling.
98 citations