An empirical validation of complexity profile graph

Yang, Jeong; Hendrix, T. Dean; Chang, Kai H.; Umphress, David

doi:10.1145/1167350.1167395

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…This way, we seek to investigate, which relations between elements in a graphical BPM are difficult to understand. In the research area of software complexity metrics, similar research has been published by Yang et al [10]. Research results suggest that local complexity metrics could be a promising predictor for understandability.…”

Section: Introductionmentioning

confidence: 57%

Cognitive Complexity in Business Process Modeling

Figl

Laue²

2011

Lecture Notes in Computer Science

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Although (business) process models are frequently used to promote human understanding of processes, practice shows that understanding complex models soon reach cognitive limits. The aim of this paper is to investigate the cognitive difficulty of understanding different relations between model elements. To allow for empirical assessment of this research question we systematically constructed model sets and comprehension questions. The results of an empirical study with 199 students tend to suggest that comprehension questions on order and concurrency are easier to answer than on repetition and exclusiveness. Additionally, results lend support to the hypothesis that interactivity of model elements influences cognitive difficulty. While our findings shed light on human comprehension of process models, they also contribute to the question on how to assure understandability of models in practice.

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Section: Introductionmentioning

confidence: 57%

Cognitive Complexity in Business Process Modeling

Figl

Laue²

2011

Lecture Notes in Computer Science

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“…Yang et al, 2005). In that context, researchers have, for instance, identified measures that assign complexity values to portions of the code.…”

Section: Introductionmentioning

confidence: 99%

Influence factors for local comprehensibility of process models

Figl¹,

Laue²

2015

International Journal of Human-Computer Studies

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“…As shown in Figure 5, in the truncation line analysis method, the truncation line is delineated in the continuous space, the variable values on the line are extracted by uniform sampling, and the curve graph of the values of multiple variables on the line is drawn. By plotting the values of multiple variables on the same line, the correlation of different variables at different positions on the line can be visually observed, and this method has been widely used in many studies to analyze the relationship between multiple variables [37,38]. In this study, the correlation, spatial differentiation, and anisotropy of variables were analyzed by selecting truncation lines from different directions.…”

Section: The Truncation Line Analysis Methodsmentioning

confidence: 99%

Assessing Spatial Variation and Driving Factors of Available Phosphorus in a Hilly Area (Gaozhou, South China) Using Modeling Approaches and Digital Soil Mapping

Zhang

Cheng

et al. 2023

Agriculture

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Soil fertility plays a crucial role in crop growth, so it is important to study the spatial distribution and variation of soil fertility for agricultural management and decision-making. However, traditional methods for assessing soil fertility are time-consuming and economically burdensome. Moreover, it is hard to capture the spatial variation of soil properties across continuous geographic space using the conventional methods. As key techniques of digital soil mapping (DSM), spatial interpolation techniques have been widely applied in soil surveys and analysis in recent years, since they can predict soil properties at unknown points in continuous space based on limited sample points. However, further research is needed on spatial interpolation models for DSM in regions with variable climates and complex terrains, which are characterized by strong spatial variation in both environmental variables and soil fertility. In this study, taking a typical hilly area in a subtropical monsoon climate, i.e., Gaozhou, Guangdong Province, China, as an example, the performances of four popular spatial interpolation models (Random Forest (RF), Ordinary Kriging, Inverse Distance Weighting, and Radial Basis Function) for digital soil mapping on available phosphorus (AP) are compared. Based on RF, the spatial variation and its driving factors of the AP of Gaozhou are then analyzed. Furthermore, by selecting three typical truncation lines from different directions, the correlations between environmental variables and AP in different spatial positions are demonstrated. The root mean square error (RMSE) results of the above four models are 32.01, 32.08, 32.74, and 33.08, respectively, which indicate that the RF has a higher interpolation accuracy. Based on the mapping results of RF, the minimum, maximum, and mean values of AP in the study area are 38.90, 95.24, and 64.96 mg/kg, respectively. The high-value areas of AP are mainly distributed in forested and orchard areas, while the low-value areas are primarily found in urban and cultivated areas in the eastern and western regions. Vegetation and topography are identified as the key factors shaping the spatial variations of AP in the study area. Furthermore, the spatial heterogeneity of the influence strength of altitude and EVI is revealed, providing a new direction for further research on DSM in the future, i.e., spatial interpolation models considering the spatial heterogeneity of the influence of environmental variables.

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An empirical validation of complexity profile graph

Cited by 4 publications

References 8 publications

Cognitive Complexity in Business Process Modeling

Cognitive Complexity in Business Process Modeling

Influence factors for local comprehensibility of process models

Assessing Spatial Variation and Driving Factors of Available Phosphorus in a Hilly Area (Gaozhou, South China) Using Modeling Approaches and Digital Soil Mapping

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