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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
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Proceedings ArticleDOI
05 Jul 2010
TL;DR: This work addresses the problem of runtime governance of service processes deployed outside of the organizations and serves multiple process clients by introducing a policy-oriented aspectual business process framework.
Abstract: With the emergence of Business Process Outsourcing and Cloud Computing, enterprises are looking for available business processes outside of their organizations to quickly adopt to new business requirements and also reduce process development and maintenance costs. The process execution needs to be governed as policy enforcement might differ between different clients. Since a process is deployed outside of the organizations and serves multiple process clients, distribution and multi-tenancy have become two requirements for runtime governance of service processes. We address this problem by introducing a policy-oriented aspectual business process framework. The runtime governance from process clients are integrated as aspects through dynamic weaving into process execution.

66 citations

Journal ArticleDOI
TL;DR: In this article, three alternative processes for the production of liquid transportation biofuels from sugar cane bagasse were compared, on the perspective of energy efficiencies using process modelling, Process Environmental Assessments and Life Cycle Assessment.

65 citations

Journal ArticleDOI
TL;DR: This paper investigates a multiple view aware approach to trace clustering, based on a co-training strategy, and shows that the presented algorithm is able to discover a clustering pattern of the log, such that related traces result appropriately clustered.
Abstract: Process mining refers to the discovery, conformance, and enhancement of process models from event logs currently produced by several information systems (e.g. workflow management systems). By tightly coupling event logs and process models, process mining makes it possible to detect deviations, predict delays, support decision making, and recommend process redesigns.Event logs are data sets containing the executions (called traces) of a business process. Several process mining algorithms have been defined to mine event logs and deliver valuable models (e.g. Petri nets) of how logged processes are being executed. However, they often generate spaghetti-like process models, which can be hard to understand. This is caused by the inherent complexity of real-life processes, which tend to be less structured and more flexible than what the stakeholders typically expect. In particular, spaghetti-like process models are discovered when all possible behaviors are shown in a single model as a result of considering the set of traces in the event log all at once.To minimize this problem, trace clustering can be used as a preprocessing step. It splits up an event log into clusters of similar traces, so as to handle variability in the recorded behavior and facilitate process model discovery. In this paper, we investigate a multiple view aware approach to trace clustering, based on a co-training strategy. In an assessment, using benchmark event logs, we show that the presented algorithm is able to discover a clustering pattern of the log, such that related traces result appropriately clustered. We evaluate the significance of the formed clusters using established machine learning and process mining metrics.

65 citations

01 Jan 2011
TL;DR: In this article, the authors present an indexing structure to support the fast detection of clones in large process model repositories, based on a combination of a method for process model decomposition (specifically the refined process structure tree), with established graph canonization and string matching techniques.
Abstract: Over time, process model repositories tend to accumulate duplicate fragments (also called clones) as new process models are created or extended by copying and merging fragments from other models. This phenomenon calls for methods to detect clones in process models, so that these clones can be refactored as separate subprocesses in order to improve maintainability. This paper presents an indexing structure to support the fast detection of clones in large process model repositories. The proposed index is based on a novel combination of a method for process model decomposition (specifically the Refined Process Structure Tree), with established graph canonization and string matching techniques. Experiments show that the algorithm scales to repositories with hundreds of models. The experimental results also show that a significant number of non-trivial clones can be found in process model repositories taken from industrial practice. © 2011 Springer-Verlag.

65 citations

Book ChapterDOI
25 Jun 2012
TL;DR: This paper defines an approach which automatically transforms BPMN process models into natural language texts and combines different techniques from linguistics and graph decomposition in a flexible and accurate manner.
Abstract: Process Modeling is a widely used concept for understanding, documenting and also redesigning the operations of organizations. The validation and usage of process models is however affected by the fact that only business analysts fully understand them in detail. This is in particular a problem because they are typically not domain experts. In this paper, we investigate in how far the concept of verbalization can be adapted from object-role modeling to process models. To this end, we define an approach which automatically transforms BPMN process models into natural language texts and combines different techniques from linguistics and graph decomposition in a flexible and accurate manner. The evaluation of the technique is based on a prototypical implementation and involves a test set of 53 BPMN process models showing that natural language texts can be generated in a reliable fashion.

65 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202359
2022184
2021254
2020327
2019368
2018395