scholarly journals Process Model Comparison Based on Cophenetic Distance

2016 ◽  
pp. 141-158 ◽  
Author(s):  
David Sánchez-Charles ◽  
Victor Muntés-Mulero ◽  
Josep Carmona ◽  
Marc Solé
Keyword(s):  
Process Model ◽  
Model Comparison

Identifying the elements of entrepreneurial opportunity constructs

2016 ◽  
Vol 17 (4) ◽  
pp. 240-255 ◽  
Author(s):  
David J. Hansen ◽  
Javier Monllor ◽  
Rodney C. Shrader
Keyword(s):  
Process Model ◽  
Model Comparison ◽  
Future Research ◽  
Integrated Process ◽  
Entrepreneurial Opportunity ◽  
Important Phenomenon ◽  
Integrated Process Model

There is plenty of debate in the entrepreneurship literature regarding entrepreneurial opportunity. There also has been a lack of construct clarity. These two issues have combined to stifle progress in understanding this important phenomenon. We believe that across these debates there are many underlying commonalities and potential for more clear constructs. In this article, we review how scholars have defined and operationalized entrepreneurial opportunity and opportunity-related processes in order to better understand what they really mean when they say ‘opportunity’. We found a total of 102 definitions and 51 operationalizations from 105 articles published in leading entrepreneurship and management journals. A total of 81 elements were identified across the definitions and operationalizations and compiled into an integrated process model. The model incorporates what seemed to be disparate views into a single unifying model. Comparison between conceptual definitions and operationalizations reveals many elements that are missing either conceptual or empirical attention. The model will help scholars more easily identify and build upon prior research. To that effect, numerous suggestions for future research are discussed and are summarized in a table.

Process model comparison and transferability across bioreactor scales and modes of operation for a mammalian cell bioprocess

2012 ◽  
Vol 29 (1) ◽  
pp. 186-196 ◽  
Author(s):  
Stephen Craven ◽  
Nishikant Shirsat ◽  
Jessica Whelan ◽  
Brian Glennon
Keyword(s):  
Mammalian Cell ◽  
Process Model ◽  
Model Comparison ◽  
Modes Of Operation

From Semantically Abstracted Traces to Process Mining and Process Model Comparison

2018 ◽  
pp. 47-59
Author(s):  
Giorgio Leonardi ◽  
Manuel Striani ◽  
Silvana Quaglini ◽  
Anna Cavallini ◽  
Stefania Montani
Keyword(s):  
Process Model ◽  
Model Comparison ◽  
Process Mining

scholarly journals VARIATIVE GEOMETRIC ALGORITHMS FOR MODELING MULTIFACTOR PROCESSES

2021 ◽  
pp. 135-145
Author(s):  
I. V. Seleznev ◽  
E. V. Konopatskiy ◽  
O. S. Voronova
Keyword(s):  
Geometric Modeling ◽  
Process Model ◽  
Model Comparison ◽  
Response Surfaces ◽  
Geometric Algorithms ◽  
Computational Experiments ◽  
Geometric Models ◽  
Variable Approach ◽  
Multidimensional Interpolation

The work is investigated by the influence of variable geometric algorithms in modeling multifactor processes using multidimensional interpolation. Geometric models of multifactorial processes obtained using multidimensional interpolation inherent variability, which is a consequence of the multiplicity of the choice of reference lines during the development of geometric modeling schemes. At the same time, all possible variations of geometric interpolyns are fully satisfying the initial data. It has been established that the number of variations of geometric schemes directly depends on the number of current parameters and the dimension of the space in which the simulated geometrical object is located. Thus, a variable approach to geometrical modeling of multifactor processes generates a number of scientific tasks, the main one is the need to determine the effect of the variability of geometric algorithms on the final results of the computational experiment and, as a result, the choice of the best modeling results. To this end, the article presents the studies of variable geometric algorithms and computational experiments on the example of 2-parametric geometric interpolyns. A classification of 2-parametric geometric interpolytesses, which were conditionally divided into 3 types. Depending on the geometric scheme of constructing interpolynta, the square geometric scheme, a rectangular geometric scheme, a mixed geometric scheme. As a result of computational experiments, it was found that for a square geometric scheme, the variability does not affect the final results, in rectangular geometric schemes, the variability has a slight influence, and mixed geometric schemes may have significant differences and require additional research to select the highest quality geometric process model. Comparison of geometric models were performed by the methods of scientific visualization by overlaying the response surfaces on each other.

scholarly journals On the Suitability of Generalized Behavioral Profiles for Process Model Comparison

2016 ◽  
pp. 13-28
Author(s):  
Abel Armas-Cervantes ◽  
Marlon Dumas ◽  
Luciano García-Bañuelos ◽  
Artem Polyvyanyy
Keyword(s):  
Process Model ◽  
Model Comparison ◽  
Behavioral Profiles

scholarly journals terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution

2018 ◽  
Author(s):  
Katherine R. Barnhart ◽  
Rachel C. Glade ◽  
Charles M. Shobe ◽  
Gregory E. Tucker
Keyword(s):  
Process Model ◽  
Model Comparison ◽  
Drainage Basin ◽  
Process Models ◽  
Numerical Comparison ◽  
Earth Surface ◽  
Model Base ◽  
Surface Water Hydrology ◽  
Binary Choices ◽  
Model Programs

Abstract. Models of landscape evolution provide insight into the development of specific field areas, create testable predictions of landform development, demonstrate the consequences of current geomorphic process theory, and spark imagination through hypothetical scenarios. While the last four decades have brought the proliferation of many alternative formulations for the redistribution of mass by Earth surface processes, relatively few studies have systematically compared and tested these alternative equations. We present a new Python modeling package, terrainbento 1.0, that enables multi-model comparison, sensitivity analysis, and calibration of Earth surface process models. terrainbento provides a set of 28 model programs that implement alternative transport laws related to four model elements: hillslope processes, surface-water hydrology, erosion by flowing water, and material properties. The 28 model programs stem from 13 binary choices related to one of these four elements – for example, the use of linear or non-linear hillslope diffusion. terrainbento is an extensible framework: model base classes that treat the elements common to all models (such as input/output and boundary conditions) make it possible to create a new model without re-inventing these common methods. terrainbento is built on top of the Landlab framework, such that new Landlab components directly support the creation of new terrainbento models. terrainbento is fully documented, has 100 % unit test coverage including numerical comparison with % all available analytical solutions for process models, and continuous integration testing. We support future users and developers with introductory Jupyter notebooks and a template for creating new terrainbento model programs. In this paper, we describe the package structure, process model theory, and software implementation of terrainbento. Finally, we illustrate the utility of terrainbento with a benchmark example highlighting the differences in steady state topography between five different process models.

scholarly journals Comparing two neurocognitive models of self-control during dietary decisions

2019 ◽  
Vol 14 (9) ◽  
pp. 957-966 ◽  
Author(s):  
Danielle Cosme ◽  
Rita M Ludwig ◽  
Elliot T Berkman
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Process Model ◽  
Model Comparison ◽  
Choice Model ◽  
Well Being ◽  
Brain Regions ◽  
Self Control ◽  
Dual Process ◽  
Dorsolateral Prefrontal

Abstract Self-control is the process of favoring abstract, distal goals over concrete, proximal goals during decision-making and is an important factor in health and well-being. We directly compare two prominent neurocognitive models of human self-control with the goal of identifying which, if either, best describes behavioral and neural data of dietary decisions in a large sample of overweight and obese adults motivated to eat more healthfully. We extracted trial-by-trial estimates of neural activity during incentive-compatible choice from three brain regions implicated in self-control, dorsolateral prefrontal cortex, ventral striatum and ventromedial prefrontal cortex and assessed evidence for the dual-process and value-based choice models of self-control using multilevel modeling. Model comparison tests revealed that the value-based choice model outperformed the dual-process model and best fit the observed data. These results advance scientific knowledge of the neurobiological mechanisms underlying self-control-relevant decision-making and are consistent with a value-based choice model of self-control.

scholarly journals Automatic Process Comparison for Subpopulations: Application in Cancer Care

2020 ◽  
Vol 17 (16) ◽  
pp. 5707
Author(s):  
Francesca Marazza ◽  
Faiza Allah Bukhsh ◽  
Jeroen Geerdink ◽  
Onno Vijlbrief ◽  
Shreyasi Pathak ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Cancer Patients ◽  
Process Model ◽  
Model Comparison ◽  
User Study ◽  
Process Models ◽  
Automatic Process ◽  
Breast Cancer Patients ◽  
Health Records ◽  
Electronic Health

Processes in organisations, such as hospitals, may deviate from the intended standard processes, due to unforeseeable events and the complexity of the organisation. For hospitals, the knowledge of actual patient streams for patient populations (e.g., severe or non-severe cases) is important for quality control and improvement. Process discovery from event data in electronic health records can shed light on the patient flows, but their comparison for different populations is cumbersome and time-consuming. In this paper, we present an approach for the automatic comparison of process models that were extracted from events in electronic health records. Concretely, we propose comparing processes for different patient populations by cross-log conformance checking, and standard graph similarity measures obtained from the directed graph underlying the process model. We perform a user study with 20 participants in order to obtain a ground truth for similarity of process models. We evaluate our approach on two data sets, the publicly available MIMIC database with the focus on different cancer patients in intensive care, and a database on breast cancer patients from a Dutch hospital. In our experiments, we found average fitness to be a good indicator for visual similarity in the ZGT use case, while the average precision and graph edit distance are strongly correlated with visual impression for cancer process models on MIMIC. These results are a call for further research and evaluation for determining which similarity or combination of similarities is needed in which type of process model comparison.

scholarly journals Comparing two neurocognitive models of self-control during dietary decisions

2019 ◽  
Author(s):  
Danielle Cosme ◽  
Rita M. Ludwig ◽  
Elliot Berkman
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Process Model ◽  
Model Comparison ◽  
Choice Model ◽  
Well Being ◽  
Brain Regions ◽  
Self Control ◽  
Dual Process ◽  
Dorsolateral Prefrontal

Self-control is the process of favoring abstract, distal goals over concrete, proximal goals during decision making, and is an important factor in health and well-being. We directly compare two prominent neurocognitive models of human self-control with the goal of identifying which, if either, best describes behavioral and neural data of dietary decisions in a large sample of overweight and obese adults motivated to eat more healthfully. We extracted trial-by-trial estimates of neural activity during incentive-compatible choice from three brain regions implicated in self-control, dorsolateral prefrontal cortex, ventral striatum, and ventromedial prefrontal cortex, and assessed evidence for the dual-process and value-based choice models of self-control using multilevel modeling. Model comparison tests revealed that the value-based choice model outperformed the dual-process model, and best fit the observed data. These results advance scientific knowledge of the neurobiological mechanisms underlying self-control relevant decision making and are consistent with a value-based choice model of self-control.

scholarly journals Bayesian Estimation in Hierarchical Models

2015 ◽  
Author(s):  
John K. Kruschke ◽  
Wolf Vanpaemel
Keyword(s):  
Bayesian Estimation ◽  
Hierarchical Models ◽  
Cognitive Process ◽  
Process Model ◽  
Model Comparison ◽  
Hierarchical Modeling ◽  
Bayesian Data Analysis ◽  
Bayesian Model Comparison ◽  
Multiple Levels ◽  
Parameter Values

Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data structures with multiple levels, such as data from individuals who are members of groups which in turn are in higher-level organizations. Hierarchical models have parameters that meaningfully describe the data at their multiple levels and connect information within and across levels. Bayesian methods are very flexible and straightforward for estimating parameters of complex hierarchical models (and simpler models too). We provide an introduction to the ideas of hierarchical models and to the Bayesian estimation of their parameters, illustrated with two extended examples. One example considers baseball batting averages of individual players grouped by fielding position. A second example uses a hierarchical extension of a cognitive process model to examine individual differences in attention allocation of people who have eating disorders. We conclude by discussing Bayesian model comparison as a case of hierarchical modeling.

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