scholarly journals An Efficient Query Optimizer with Materialized Intermediate Views in Distributed and Cloud Environment

2021 ◽  
Vol 15 (1) ◽  
pp. 105-111
Author(s):  
Archana Bachhav ◽  
Vilas Kharat ◽  
Madhukar Shelar
Keyword(s):  
Large Scale ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Algorithm Efficiency ◽  
Complex Queries ◽  
Relational Database System ◽  
Resource Requirements ◽  
Common Subexpressions ◽  
Intermediate Views ◽  
By Products

In cloud computing environment hardware resources required for the execution of query using distributed relational database system are scaled up or scaled down according to the query workload performance. Complex queries require large scale of resources in order to complete their execution efficiently. The large scale of resource requirements can be reduced by minimizing query execution time that maximizes resource utilization and decreases payment overhead of customers. Complex queries or batch queries contain some common subexpressions. If these common subexpressions evaluated once and their results are cached, they can be used for execution of further queries. In this research, we have come up with an algorithm for query optimization, which aims at storing intermediate results of the queries and use these by-products for execution of future queries. Extensive experiments have been carried out with the help of simulation model to test the algorithm efficiency

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.

A MapReduce Iteration Framework in Local Parallel and Message Synchronization

2013 ◽  
Vol 380-384 ◽  
pp. 2237-2241 ◽  
Author(s):  
Ting Wang ◽  
Hua Liang Zhang ◽  
Peng Zeng
Keyword(s):  
Distributed Algorithm ◽  
Large Scale ◽  
Massive Data ◽  
Computing Environment ◽  
Operating Environment ◽  
Cloud Computing Environment ◽  
Synchronization Mechanism ◽  
Computing Model ◽  
Parallel Layer ◽  
Mapreduce Model

With the development of large-scale distributed computing, Stand-alone operating environment to meet the demand of the time and space overhead of massive data based on. There is more attention to how to design the distributed algorithm for efficient cloud computing environment. The MapReduce model cannot solve the issue. In this paper, the redesign of the computing model of MapReduce, ensure the existing calculation models compatible with the old MapReduce operation. At the same time, the framework used the message synchronization mechanism to implement state data changing interaction tasks in Parallel Layer. Compared to the original MapReduce operation, greatly reduces the processing time of the MapReduce iterative algorithm.

Process virtualization of large-scale lidar data in a cloud computing environment

2013 ◽  
Vol 60 ◽  
pp. 109-116 ◽  
Author(s):  
Haiyan Guan ◽  
Jonathan Li ◽  
Liang Zhong ◽  
Yu Yongtao ◽  
Michael Chapman
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Lidar Data ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Process Virtualization

A survey of prediction-based resource scheduling techniques for physics-based scientific applications

2018 ◽  
Vol 32 (25) ◽  
pp. 1850295 ◽  
Author(s):  
Gurleen Kaur ◽  
Anju Bala
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Scientific Applications ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Community Needs ◽  
Data Platform ◽  
Disciplinary Expertise ◽  
Resource Prediction ◽  
Real World Problems

The state-of-the-art physics alliances have augmented various opportunities to solve complex real-world problems. These problems require both multi-disciplinary expertise as well as large-scale computational experiments. Therefore, the physics community needs a flexible platform which can handle computational challenges such as volume of data, platform heterogeneity, application complexity, etc. Cloud computing provides an incredible amount of resources for scientific users on-demand, thus, it has become a potential platform for executing scientific applications. To manage the resources of Cloud efficiently, it is required to explore the resource prediction and scheduling techniques for scientific applications which can be deployed on Cloud. This paper discusses an extensive analysis of scientific applications, resource predictions and scheduling techniques for Cloud computing environment. Further, the trend of resource prediction-based scheduling and the existing techniques have also been studied. This paper would be helpful for the readers to explore the significance of resource prediction-based scheduling techniques for physics-based scientific applications along with the associated challenges.

SWSA: A Hybrid Scientific Workflow Scheduling Algorithm based on Metaheuristic Approach in Cloud Computing Environment

2021 ◽  
pp. 2150035
Author(s):  
Leyli Abbasi ◽  
Hossien Momeni ◽  
Mehdi Yaghoubi
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Initial Population ◽  
Large Set ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Ranking Tasks

The cloud computing environment with a set of distributed computing resources is a suitable platform for the execution of large-scale applications. One of these applications is scientific workflow applications in which a large set of interrelated tasks are executed for a certain purpose. Scientific workflow scheduling is one of the main challenges in this area, which aims at the optimal assignment of tasks to computational resources. Given the heterogeneity of cloud computing resources, the scientific workflow scheduling is an NP-Complete problem that can be solved by heuristic methods. In this paper, an improved evolutionary algorithm called Scientific Workflow Scheduling Algorithm (SWSA) for scheduling scientific workflows in the cloud will be provided by ranking tasks and improving the initial population of tasks. The objective of this algorithm is to create a balance and an improvement in the parameters of the execution cost and workflow execution completion time. In this proposed approach, a heuristic algorithm is used to rank and generate the initial population, which increases the convergence rate. The experimental results show that SWSA is more efficient in terms of cost and execution time compared with other approaches.

Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing Environment

2021 ◽  
pp. 751-769
Author(s):  
Xinling Tang ◽  
Hongyan Xu ◽  
Yonghong Tan ◽  
Yanjun Gong
Keyword(s):  
Cloud Computing ◽  
Collaborative Filtering ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Distributed Storage ◽  
Distributed Processing ◽  
Storage System ◽  
Personalized Recommendation ◽  
Computing Environment ◽  
Cloud Computing Environment

With the advent of cloud computing era and the dramatic increase in the amount of data applications, personalized recommendation technology is increasingly important. However, due to large scale and distributed processing architecture and other characteristics of cloud computing, the traditional recommendation techniques which are applied directly to the cloud computing environment will be faced with low recommendation precision, recommended delay, network overhead and other issues, leading to a sharp decline in performance recommendation. To solve these problems, the authors propose a personalized recommendation collaborative filtering mechanism RAC in the cloud computing environment. The first mechanism is to develop distributed score management strategy, by defining the candidate neighbors (CN) concept screening recommended greater impact on the results of the project set. And the authors build two stage index score based on distributed storage system, in order to ensure the recommended mechanism to locate the candidate neighbor. They propose collaborative filtering recommendation algorithm based on the candidate neighbor on this basis (CN-DCF). The target users are searched in candidate neighbors by the nearest neighbor k project score. And the target user's top-N recommendation sets are predicted. The results show that in the cloud computing environment RAC has a good recommendation accuracy and efficiency recommended.

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