A service-oriented dynamic multi-level maintenance grouping strategy based on prediction information of multi-component systems

2019 ◽  
Vol 53 ◽  
pp. 49-61 ◽  
Author(s):  
Fengtian Chang ◽  
Guanghui Zhou ◽  
Chao Zhang ◽  
Zhongdong Xiao ◽  
Chuang Wang
Keyword(s):  
Grouping Strategy ◽  
Component Systems ◽  
Service Oriented ◽  
Multi Level

scholarly journals Teacher Careers in Education Systems That Are Coherent for Learning: Choose and Curate Toward Commitment to Capable and Committed Teachers (5Cs)

2021 ◽  
Author(s):  
Yue-Yi Hwa ◽  
Lant Pritchett
Keyword(s):  
Student Learning ◽  
Teaching Profession ◽  
Status Quo ◽  
Academic Disciplines ◽  
Education Systems ◽  
School Enrolment ◽  
Middle Income ◽  
Wide Range ◽  
Component Systems ◽  
Multi Level

How can education authorities and organisations develop empowered, highly respected, strongly performance-normed, contextually embedded teaching professionals who cultivate student learning? This challenge is particularly acute in many low- and middle-income education systems that have successfully expanded school enrolment but struggle to help children master even the basics of reading, writing, and arithmetic. In this primer, we synthesise research from a wide range of academic disciplines and country contexts, and we propose a set of principles for guiding the journey toward an empowered, effective teaching profession. We call these principles the 5Cs: choose and curate toward commitment to capable and committed teachers. These principles are rooted in the fact that teachers and their career structures are embedded in multi-level, multi-component systems that interact in complex ways. We also outline five premises for practice, each highlighting an area in which education authorities and organisations can change the typical status quo approach in order to apply the 5Cs and realise the vision of empowered teaching profession.

A Service-Oriented Foundation for Big Data

2020 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Technological Characteristics ◽  
Service Oriented ◽  
Technological Level ◽  
Multi Level ◽  
Big Service

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

Multi-level predictive maintenance for multi-component systems

2015 ◽  
Vol 144 ◽  
pp. 83-94 ◽  
Author(s):  
Kim-Anh Nguyen ◽  
Phuc Do ◽  
Antoine Grall
Keyword(s):  
Predictive Maintenance ◽  
Component Systems ◽  
Multi Level

A Service-Oriented Foundation for Big Data

2022 ◽  
pp. 869-887
Author(s):  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Socioeconomic Level ◽  
Business Analytics ◽  
Socioeconomic Characteristics ◽  
Fundamental Level ◽  
Service Oriented ◽  
Technological Level ◽  
Multi Level ◽  
Big Service

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

Distributed Workflow Execution Optimization of SBA Based on Service-Oriented Architecture

2010 ◽  
Vol 108-111 ◽  
pp. 960-965
Author(s):  
Xiao Ping Du ◽  
Jian Jun Song ◽  
Yang Sheng Zhao
Keyword(s):  
Service Oriented Architecture ◽  
Data Transfer ◽  
Satellite Image ◽  
Workflow Technology ◽  
Loosely Coupled ◽  
Grouping Strategy ◽  
Grouping Strategies ◽  
Service Oriented ◽  
Speed Up ◽  
Generic Service

According to the characteristics of the information resources in modern acquisition, such as heterogeneous, distributed, loosely coupled, the problems and deficiencies of workflow technology and products were analyzed. Dynamic service grouping strategies based on SOA was proposed and was proved in theory. In addition, the workflow architecture based on SOA was built and the workflow engine of simulation-based acquisition (SBA) was designed. Therefore, the legacy code can be wrapped and the service can be grouped dynamically from the dynamic generic service factory. Grouping strategy was proved according to the SPOT satellite image application which was part of the SBA toolbox on grid. Experimental results show that: services grouping of a workflow can reduce the total overhead induced by the submission, scheduling, queuing and data transfer time. On a production grid infrastructure, the optimization proposed introduces a significant speed-up when compared to a traditional execution. The range is from 1.21 to 2.97.

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