Large-scale Ship Fault Data Retrieval Algorithm Supporting Complex Query in Cloud Computing

2019 ◽  
Vol 97 (sp1) ◽  
pp. 236
Author(s):  
Shujuan Zhang
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Data Retrieval ◽  
Retrieval Algorithm ◽  
Complex Query

scholarly journals Medical Cloud Computing Data Processing to Optimize the Effect of Drugs

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Fengxia Li ◽  
Zhi Qu ◽  
Ruiling Li
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Vascular Endothelial Cells ◽  
Senile Dementia ◽  
Data Retrieval ◽  
Strong Stability ◽  
Image Feature ◽  
Computing Platform ◽  
Image Set ◽  
The Impact

In recent years, cloud computing technology is maturing in the process of growing. Hadoop originated from Apache Nutch and is an open-source cloud computing platform. Moreover, the platform is characterized by large scale, virtualization, strong stability, strong versatility, and support for scalability. It is necessary and far-reaching, based on the characteristics of unstructured medical images, to combine content-based medical image retrieval with the Hadoop cloud platform to conduct research. This study combines the impact mechanism of senile dementia vascular endothelial cells with cloud computing to construct a corresponding data retrieval platform of the cloud computing image set. Moreover, this study uses Hadoop’s core framework distributed file system HDFS to upload images, store the images in the HDFS and image feature vectors in HBase, and use MapReduce programming mode to perform parallel retrieval, and each of the nodes cooperates with each other. The results show that the proposed method has certain effects and can be applied to medical research.

Public cloud computing for seismological research: Calculating large-scale noise cross-correlations using ALIYUN

2018 ◽  
Vol 31 (5-6) ◽  
pp. 227-233
Author(s):  
Weitao Wang ◽  
◽  
Baoshan Wang ◽  
Xiufen Zheng ◽  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Public Cloud ◽  
Cross Correlations

Predicting Node Failures in an Ultra-Large-Scale Cloud Computing Platform

2020 ◽  
Vol 29 (2) ◽  
pp. 1-24
Author(s):  
Yangguang Li ◽  
Zhen Ming (Jack) Jiang ◽  
Heng Li ◽  
Ahmed E. Hassan ◽  
Cheng He ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Computing Platform ◽  
Cloud Computing Platform ◽  
Node Failures

scholarly journals A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

2021 ◽  
Vol 13 (2) ◽  
pp. 176
Author(s):  
Peng Zheng ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yi Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Retrieval System ◽  
Hyperspectral Image ◽  
Parallel Implementation ◽  
Remotely Sensed Data ◽  
Web Interfaces ◽  
Content Retrieval ◽  
Service Mode ◽  
Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

Tangent height registration method for the Version 14 data retrieval algorithm of the solar occultation sensor ILAS-II

2007 ◽  
Vol 46 (29) ◽  
pp. 7196 ◽  
Author(s):  
Tomoaki Tanaka ◽  
Hideaki Nakajima ◽  
Takafumi Sugita ◽  
Mitsumu K. Ejiri ◽  
Hitoshi Irie ◽  
...  
Keyword(s):  
Data Retrieval ◽  
Retrieval Algorithm ◽  
Registration Method ◽  
Solar Occultation

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

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