An Organization Model based on Party Pattern to Support Dynamic Change for Role-based Workflow Application

2006 ◽  
Author(s):  
Shuren Bai ◽  
Peng Du
Keyword(s):  
Dynamic Change ◽  
Organization Model ◽  
Model Based ◽  
Role Based ◽  
Workflow Application

scholarly journals Automated Model-Based Testing of Role-Based Access Control Using Predicate/Transition Nets

2015 ◽  
Vol 64 (9) ◽  
pp. 2490-2505 ◽  
Author(s):  
Dianxiang Xu ◽  
Michael Kent ◽  
Lijo Thomas ◽  
Tejeddine Mouelhi ◽  
Yves Le Traon
Keyword(s):  
Access Control ◽  
Role Based Access Control ◽  
Model Based ◽  
Model Based Testing ◽  
Role Based

scholarly journals A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

2019 ◽  
Vol 11 (14) ◽  
pp. 3832 ◽  
Author(s):  
Pingping Xiong ◽  
Jia Shi ◽  
Lingling Pei ◽  
Song Ding
Keyword(s):  
Linear Time ◽  
Dynamic Change ◽  
Time Varying ◽  
The Novel ◽  
Model Based ◽  
Number Sequences ◽  
The Government ◽  
Grey Number ◽  
Beijing China

Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM10, SO2 and NO2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

The Design and Implementation of G-RBAC Model in Map Permission

2011 ◽  
Vol 268-270 ◽  
pp. 1457-1461
Author(s):  
Ji Qiu Deng ◽  
Xiao Qing Luo ◽  
Huang Ling Gu
Keyword(s):  
Access Control ◽  
Real Time ◽  
Spatial Data ◽  
Role Based Access Control ◽  
Design And Implementation ◽  
Model Based ◽  
Valid Method ◽  
Role Based ◽  
And Control

With the development of WebGIS, it plays a more and more important role in government and enterprises. The effective way to access and control spatial data, because of relating to the security and business secrets of government and enterprises, becomes an urgent concern for them. Now, RBAC (Role Based Access Control) can be a valid method to access and control uniform resource, but it is lack of flexibility and real-time ability to control spatial data in WebGIS. For that, this paper proposes G-RBAC model based on RBAC model to enhance the flexibility and real-time ability to access spatial data.

scholarly journals PENERAPAN PARTICLE SWARM OPTIMIZATION PADA ALGORITMA C 4.5 UNTUK SELEKSI PENERIMAAN KARYAWAN

2018 ◽  
Vol 4 (2) ◽  
pp. 51-59
Author(s):  
Agus Wiyatno
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
Classification Tree ◽  
Particle Swarm ◽  
Classification Model ◽  
Accurate Information ◽  
Swarm Optimization ◽  
Organization Model ◽  
Goods And Services ◽  
Model Based

The Employees are the most vital element of the company as they had a big contribution and involved almost for all section on how the company will go up and down. Employees and the company affect the efficiency, effectiveness,designing, producing goods and services, oversee the quality, market products, allocating financial resources, and determines the overall goals and strategies of the organization. Therefore, organizations need accurate information and sustainable in order to get suitable candidates with the qualifications of the organization. Model algorithms are widely used in research related to the employee is C4.5 decision tree classification model. Advantages of using adecision tree classification models are the result of the decision tree is simple and easy to understand. Many studies using the method of decision tree and classification tree in predicting the employees selection but results theaccuracy of the resulting value is less accurate. In this study created a C 4.5 Algorithm model and C 4.5 Algorithm model based on particle swarm optimization to get the rule in employees selection and provide a more accuratevalue of accuracy. After testing C 4.5 algorithm model based on particle swarm optimization, Implementation of particle swarm optimization can produce accuracy value of C 4.5 algorithm model from 80.80 % to 85.20 % and theAUC value from 0.878 to 0.891. By the formation the model selection of employees, the company can be helped for employee selection.

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