scholarly journals Construction of an Intelligent APP for Dance Training Mobile Information Management Platform Based on Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yan Gao ◽  
Dazhi Xu
Keyword(s):  
Information Technology ◽  
Cloud Computing ◽  
Data Acquisition ◽  
Information Management ◽  
Performing Arts ◽  
Rapid Development ◽  
Processing System ◽  
Edge Computing ◽  
System Load ◽  
Management Platform

In recent years, with the rapid development of modern technology and the continuous promotion of information technology, information technology has been widely used in modern performing arts. Information management has become the most practical and effective method and means in performing arts training management, but as the amount of various data grows exponentially, the requirements for computing processing power and speed for massive amounts of data and information are also increasing day by day. This article aims to study the use of edge computing to solve the problems of high latency and high cost when traditional cloud computing centers provide services. In response to these problems, this paper proposes a data acquisition and processing system architecture based on edge computing, which uses edge computing to mine the computing power of edge terminals in the network, performs partial or all calculations at the edge terminals, processes private data, and reduces cloud computing. The center’s computing, transmission bandwidth load, and energy consumption, combined with cloud computing, provide data acquisition, processing, and analysis solutions with low latency and high processing capabilities. This article details how to optimize edge server development to minimize access latency and consider network reliability when requesting access to edge servers. This paper uses the proposed edge server deployment algorithm and system load optimization, which can effectively reduce the network delay and system load of the edge server, and the experimental results show that the system performance is improved by 23.5% after effective optimization.

Research on Key Technologies of Massive Videos Management Under the Background of Cloud Platform

2019 ◽  
Vol 23 (1) ◽  
pp. 72-77
Author(s):  
Lin Jin ◽  
◽  
Changhong Yan
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Management System ◽  
Rapid Development ◽  
Mobile Internet ◽  
Video Data ◽  
Cloud Platform ◽  
Module Design ◽  
Management Platform ◽  
Key Technologies

With the rapid development of mobile internet and smart city, video surveillance is popular in areas such as transportation, schools, homes, and shopping malls. It is important subject to manage the massive videos quickly and accurately. This paper tries to use Hadoop cloud platform for massive video data storage, transcoding and retrieval. The key technologies of cloud computing and Hadoop are introduced firstly in the paper. Then, we analyze the functions of video management platform, such as user management, videos storage, videos transcoding, and videos retrieval. According to the basic functions and cloud computing, each module design process and figure are provided in the paper. The massive videos management system based on cloud platform will be better than the traditional videos management system in the aspects of storage capacity, transcoding performance and retrieval speed.

scholarly journals A Cuckoo Search Based Heuristic for Replicating IoT Data in Cloud Edge System

2020 ◽  
Vol 8 (4S5) ◽  
pp. 227-235
Keyword(s):  
Information Technology ◽  
Cloud Computing ◽  
Radio Frequency Identification ◽  
Cloud Storage ◽  
Large Scale ◽  
Rapid Development ◽  
Storage System ◽  
Cuckoo Search ◽  
Data Volume ◽  
High Degree

The rapid development in information technology has rendered an increase in the data volume at a speed which is surprising. In recent times, cloud computing and the Internet of Things (IoT) have become the hottest among the topics in the industry of information technology. There are many advantages to Cloud computing such as scalability, low price, and large scale and the primary technique of the IoTs like the Radio-Frequency Identification (RFID) have been applied to a large scale. In the recent times, the users of cloud storage have been increasing to a great extent and the reason behind this was the cloud storage system bringing down the issues in maintenance and also has a low amount of storage when compared to other methods. This system provides a high degree of reliability and availability where redundancy is introduced to the systems. In the replicated systems, objects get to be copied many times and every copy resides in a different location found in distributed computing. So, replication of data has been posing some threat to the cloud storage for users and also for the providers since it has been a major challenge providing efficient storage of data. So, the work has been analysing different strategies of replication of data and have pointed out several issues that are affected by this. For the purpose of this work, replication of data has been presented by employing the Cuckoo Search (CS) and the Greedy Search. The research is proceeding in a direction to reduce the replications without any adverse effect on the reliability and the availability of data.

scholarly journals Research on Informatization Strategies of Education and Teaching Management in Higher Vocational Colleges in the Era of Cloud Computing and Big Data

2020 ◽  
Vol 9 (7) ◽  
pp. 148
Author(s):  
Xiaozheng Yang
Keyword(s):  
Information Technology ◽  
Cloud Computing ◽  
Big Data ◽  
Vocational Education ◽  
Information Management ◽  
Higher Vocational Education ◽  
Teaching Management ◽  
Sustainable Future ◽  
Vocational Colleges ◽  
Teaching Information

At present, with the development of information technology, cloud computing and big data have been integrated into various industries in society, and they also have a relatively prominent performance in education and teaching management. The greatly enhanced efficiency of education and teaching information management in universities is a sustainable future for universities. The inevitable choice for sending letters. This article studies and discusses the informatization strategy of education and teaching management in higher vocational colleges in the era of cloud computing and big data, hoping to provide some useful suggestions for the development of higher vocational education in our country.

scholarly journals TRC-PAD: ACCELERATING RECRUITMENT OF AD CLINICAL TRIALS THROUGH INNOVATIVE INFORMATION TECHNOLOGY

2020 ◽  
pp. 1-8
Author(s):  
G.A. Jimenez-Maggiora ◽  
R. Raman ◽  
S. Bruschi ◽  
O. Langford ◽  
M. Donohue ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Information Technology ◽  
Clinical Trials ◽  
Information Management ◽  
Modular Design ◽  
Early Stage ◽  
Cost Effective ◽  
Management Platform ◽  
Study Participants

BACKGROUND: The Trial-Ready Cohort for Preclinical/Prodromal Alzheimer’s Disease (TRC-PAD) Informatics Platform (TRC-PAD IP) was developed to facilitate the efficient selection, recruitment, and assessment of study participants in support of the TRC-PAD program. Objectives: Describe the innovative architecture, workflows, and components of the TRC-PAD IP. Design: The TRC-PAD IP was conceived as a secure, scalable, multi-tiered information management platform designed to facilitate high-throughput, cost-effective selection, recruitment, and assessment of TRC-PAD study participants and to develop a learning algorithm to select amyloid-bearing participants to participate in trials of early-stage Alzheimer’s disease. Setting: TRC-PAD participants were evaluated using both web-based and in-person assessments to predict their risk of amyloid biomarker abnormalities and eligibility for preclinical and prodromal clinical trials. Participant data were integrated across multiple stages to inform the prediction of amyloid biomarker elevation. Participants: TRC-PAD participants were age 50 and above, with an interest in participating in Alzheimer’s research. Measurements: TRC-PAD participants’ cognitive performance and subjective memory concerns were remotely assessed on a longitudinal basis to predict participant risk of biomarker abnormalities. Those participants determined to be at the highest risk were invited to an in-clinic screening visit for a full battery of clinical and cognitive assessments and amyloid biomarker confirmation using positron emission tomography (PET) or lumbar puncture (LP). Results: The TRC-PAD IP supported growth in recruitment, screening, and enrollment of TRC-PAD participants by leveraging a secure, scalable, cost-effective cloud-based information technology architecture. Conclusions: The TRC-PAD program and its underlying information management infrastructure, TRC-PAD IP, have demonstrated feasibility concerning the program aims. The flexible and modular design of the TRC-PAD IP will accommodate the introduction of emerging diagnostic technologies.

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