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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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Book ChapterDOI
01 Sep 2008
TL;DR: This work proposes to specify an adaptive process as a set of scenarios using a Petri net syntax, and provides an adaptation operator that synthesizes and adapts the system behavior at run-time based on the given scenarios.
Abstract: In the immediate aftermath of a disaster, routine processes, even if specifically designed for such a situation, are not enacted blindly. Actions and processes rather adapt their behavior based on observations and available information. Attempts to support these processes by technology rely on process models that faithfully capture process execution and adaptation. Based on experiences from actual disaster response settings, we propose to specify an adaptive process as a set of scenarios using a Petri net syntax. Our operational model provides an adaptation operator that synthesizes and adapts the system behavior at run-time based on the given scenarios. An example illustrates our approach.

74 citations

Journal Article
TL;DR: In this article, the authors derive thresholds for a set of structural measures for predicting errors in conceptual process models, using a collection of 2,000 business process models from practice as a means of determining thresholds, applying an adaptation of the ROC curves method.
Abstract: The quality of conceptual business process models is highly relevant for the design of corresponding information systems In particular, a precise measurement of model characteristics can be beneficial from a business perspective, helping to save costs thanks to early error detection This is just as true from a software engineering point of view In this latter case, models facilitate stakeholder communication and software system design Research has investigated several proposals as regards measures for business process models, from a rather correlational perspective This is helpful for understanding, for example size and complexity as general driving forces of error probability Yet, design decisions usually have to build on thresholds, which can reliably indicate that a certain counter-action has to be taken This cannot be achieved only by providing measures; it requires a systematic identification of effective and meaningful thresholds In this paper, we derive thresholds for a set of structural measures for predicting errors in conceptual process models To this end, we use a collection of 2,000 business process models from practice as a means of determining thresholds, applying an adaptation of the ROC curves method Furthermore, an extensive validation of the derived thresholds was conducted by using 429 EPC models from an Australian financial institution Finally, significant thresholds were adapted to refine existing modeling guidelines in a quantitative way

74 citations

Journal ArticleDOI
TL;DR: A simple way of combining all the available knowledge relating to a given process is presented, including a hybrid model for state estimation and prediction on the example of a yeast production process.
Abstract: Process models are used to formulate knowledge about process behaviour. They are applied, e.g., to predict the process' future behaviour and for state estimation when reliable on-line measuring techniques to monitor the key variables of the process are not available. There are different sources of information available for modelling, which provide process knowledge in different representations. Some elements or aspects may be described by physically based mathematical models and others by heuristically obtained rules of thumb, while some information may still be hidden in the process data recorded during previous runs of the process. Heuristic rules are conveniently processed with fuzzy expert systems, while artificial neural networks present themselves as a powerful tool for uncovering the information within the process data without the need to transform the information into one of the other representations. Artificial neural networks and fuzzy technology are increasingly being employed for modelling biotechnological processes, thus extending the traditional way of process modelling by mathematical equations. However, a sufficiently comprehensive combination of all these techniques has not yet been put forward. Here, we present a simple way of combining all the available knowledge relating to a given process. In a case study, we demonstrate the development of a hybrid model for state estimation and prediction on the example of a yeast production process. The model was validated during a cultivation performed in a standard pilot-scale fermenter.

74 citations

Journal ArticleDOI
TL;DR: This paper introduces definitions of two metrics that quantify the (un)structuredness of a process model, the degree of structuredness and the unmatched connector count, and uses the event-driven process chain models of the SAP reference model for validating the capability of these metrics to predict error probability.
Abstract: Recent research has shown that business process models from practice suffer from several quality problems. In particular, the correctness of control flow has been analyzed for industry-scale collections of process models revealing that error ratios are surprisingly high. In the past the structuredness property has been discussed as a guideline to avoid errors, first in research on programming, and later also in business process modeling. In this paper we investigate the importance of structuredness for process model correctness from an empirical perspective. We introduce definitions of two metrics that quantify the (un)structuredness of a process model, the degree of structuredness and the unmatched connector count. Then, we use the event-driven process chain models of the SAP reference model for validating the capability of these metrics to predict error probability. Our findings clearly support the importance of structuredness as a design principle for achieving correctness in process models.

74 citations

Journal ArticleDOI
TL;DR: The areas of modelling distributed parameter systems, and supporting the formal description and solution of general optimisation problems are identified as being of strategic importance for the future evolution of general-purpose process modelling environments.

74 citations


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