Rule-Based Ad-Hoc Workflow Modeling for Service Coordination: A Case Study of a Telecom Operational Support System

Author(s):  
Jae-Yoon JUNG ◽  
Joonsoo BAE
Author(s):  
Wilfried Lemahieu ◽  
Monique Snoeck ◽  
Cindy Michiels

This case study presents an experience report on an enterprise modelling and application integration project for a young company, starting in the telecommunications business area. The company positions itself as a broadband application provider for the SME market. Whereas its original information infrastructure consisted of a number of stand-alone business and operational support system (BSS/OSS) applications, the project’s aim was to define and implement an enterprise layer, serving as an integration layer on top of which these existing BSS/OSSs would function independently and in parallel. This integration approach was to be non intrusive and was to use the business applications as-is. The scope of the case entails the conception of a unifying enterprise model and the formulation of an implementation architecture for the enterprise layer, based on the enterprise JavaBeans framework.


Author(s):  
Wilfried Lemahieu ◽  
Monigue Snoeck ◽  
Cindy Michiels

This case study presents an experience report on an Enterprise Modelling and Application Integration project for a young company, starting in the telecommunications business area. The company positions itself as a broadband application provider for the SME market. Whereas its original information infrastructure consisted of a number of stand-alone business and operational support system (BSS/OSS) applications, the projects aim was to define and implement an Enterprise Layer, serving as an integration layer on top of which these existing BSS/OSSs would function independently and in parallel. This integration approach was to be nonintrusive and was to use the business applications as-is. The scope of the case entails the conception of a unifying Enterprise Model and the formulation of an implementation architecture for the Enterprise Layer, based on the Enterprise JavaBeans framework.


2015 ◽  
Vol 6 (1) ◽  
pp. 12-19
Author(s):  
Angellia Debora Suryawan ◽  
Marlene Martani ◽  
Mahenda Metta Surya

Human resources are an important asset in the entire company operations activity. A human resources management support system should be provided to improve performance in  accordance with the company target. The purpose of this study is to design a model of operational and human resource management support systems that can integrate employee performance data, simplify management of employee data, and generate reports in the form of Key Performance Indicator (KPI) and Binusian Level. Methodology used in this study is using literature study, design, and test a model to make operational and human resource management information system. Index Terms - human resources, operational support system, Key Performance Indicator (KPI)  


Relay Journal ◽  
2020 ◽  
pp. 66-79
Author(s):  
Mizuki Shibata ◽  
Chihiro Hayashi ◽  
Yuri Imamura

This paper reports on a case study of learner-led study-abroad events in the language learning space at a Japanese University. We present multiple reflections on the events from different perspectives: the event organizer (student), an administrative staff member, and a learning advisor working at the center. We also introduce the support system that a group of administrative staff members and learning advisors are in charge of helping learners to hold their events. Moreover, throughout our reflections, several factors that made the learner-led study-abroad events sustainable and successful are demonstrated.


2021 ◽  
Vol 11 (13) ◽  
pp. 5810
Author(s):  
Faisal Ahmed ◽  
Mohammad Shahadat Hossain ◽  
Raihan Ul Islam ◽  
Karl Andersson

Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.


Author(s):  
Suzanne L. van Winkel ◽  
Alejandro Rodríguez-Ruiz ◽  
Linda Appelman ◽  
Albert Gubern-Mérida ◽  
Nico Karssemeijer ◽  
...  

Abstract Objectives Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist. Methods A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance. Results On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced (p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115). Conclusions Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. Key Points • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.


Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 480
Author(s):  
Martina Zappaterra ◽  
Laura Menchetti ◽  
Leonardo Nanni Costa ◽  
Barbara Padalino

This study aimed at documenting whether dromedary camels have a preference for shade and how their behavior would change depending on the presence of shade and variable space allowance. A total of 421 animals kept in 76 pens (66 with shelter (Group 1), and 10 without shelter (Group 2)) at the camel market in Doha (Qatar) were recorded for 1 min around 11:00 a.m. when the temperature was above 40 °C. The number of animals in the sun and shade and their behaviors were analyzed using an ad libitum sampling method and an ad hoc ethogram. The results of a chi-square test indicated that camels in Group 1 had a clear preference for shade (p < 0.001). The majority of Group 1 camels were indeed observed in the shade (312/421; 74.11%). These camels spent more time in recumbency and ruminating, while standing, walking, and self-grooming were more commonly expressed by the camels in the sun (p < 0.001). Moreover, locomotory stereotypic behaviors (i.e., pacing) increased as space allowance decreased (p = 0.002). Based on the findings of this pilot study, camels demonstrated a preference for shade; shade seemed to promote positive welfare, while overcrowding seemed to trigger stereotypy and poor welfare. Overall, our preliminary results are novel and provide evidence that shaded areas are of paramount importance for camel welfare. Further research, involving designed studies at multiple locations is needed to confirm these results.


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