scholarly journals Research on Query Optimization of Classic Art Database Based on Artificial Intelligence and Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiang Dong ◽  
Lijia Zeng
Keyword(s):  
Query Optimization ◽  
Optimization Algorithm ◽  
System Performance ◽  
Database Queries ◽  
Graph Data ◽  
Modern Information Technology ◽  
Data Query ◽  
Processing Power ◽  
Additional Task ◽  
The Cost

With the changes and development of the social era, my country’s classic art is slowly being lost. In order to more effectively inherit and preserve classic art, the collection and sorting of classic art data through modern information technology has become a top priority. Database storage is a good way. However, as the amount of data grows, the requirements for computing processing power and query speed for massive amounts of data and information are also increasing day by day. Faced with this problem, this article is aimed at studying the optimization of database queries through effective algorithms to improve the efficiency of data query. Based on the traditional database query optimization algorithm, this article improves on the traditional algorithm and proposes a semi-join query optimization algorithm, which reduces the number of connection cards and the number of columns and uses the number of blocks that participate in the semi-link algorithm connection and preconnection preview and selection. And other functions reduce the size of the participating block, and the connection sent between sites reduces the cost of sending between networks. The graph data query optimization algorithm is used to optimize the graph data query in the database to reduce the extra task overhead and improve the system performance. The experimental results of this paper show that through the data query optimization algorithm of this paper, the additional task overhead is reduced by 19%, the system performance is increased by 22%, and the data query efficiency is increased by 31%.

Multi-dimensional query optimization algorithm for bitmap index with binning

2010 ◽  
Vol 30 (8) ◽  
pp. 2013-2016 ◽  
Author(s):  
Li-ming WANG ◽  
Xiao CHENG ◽  
Yu-mei CHAI
Keyword(s):  
Query Optimization ◽  
Optimization Algorithm ◽  
Bitmap Index

A Determinant Elimination Method for Bottleneck Assignment and Generalized Assignment Problems

2012 ◽  
Vol 239-240 ◽  
pp. 1522-1527
Author(s):  
Wen Bo Wu ◽  
Yu Fu Jia ◽  
Hong Xing Sun
Keyword(s):  
Computational Complexity ◽  
Optimization Algorithm ◽  
Assignment Problem ◽  
Numerical Experiments ◽  
High Efficiency ◽  
Synthesis Method ◽  
Assignment Problems ◽  
Generalized Assignment ◽  
The Cost ◽  
Time And Space Complexity

The bottleneck assignment (BA) and the generalized assignment (GA) problems and their exact solutions are explored in this paper. Firstly, a determinant elimination (DE) method is proposed based on the discussion of the time and space complexity of the enumeration method for both BA and GA problems. The optimization algorithm to the pre-assignment problem is then discussed and the adjusting and transformation to the cost matrix is adopted to reduce the computational complexity of the DE method. Finally, a synthesis method for both BA and GA problems is presented. The numerical experiments are carried out and the results indicate that the proposed method is feasible and of high efficiency.

Efficient Solar Desalination System Using Humidification/Dehumidification Process

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Adel M. Abdel Dayem
Keyword(s):  
High Pressure ◽  
Heat Source ◽  
System Performance ◽  
Salt Water ◽  
Weather Conditions ◽  
Water Production ◽  
High Pressure Pump ◽  
Solar Desalination ◽  
Flat Plate Collector ◽  
The Cost

An innovative solar desalination system is successfully designed, manufactured, and experimentally tested at Makkah, 21.4 degN. The system consists of 1.15 m2 flat-plate collector as a heat source and a desalination unit. The unit is about 400 l vertical cylindrical insulated tank. It includes storage, evaporator, and condenser of hot salt-water that is fed from the collector. The heated water in the collector is raised naturally to the unit bottom at which it is used as storage. A high pressure pump is used to inject the water vertically up through 1-mm three nozzles inside the unit. The hot salt-water is atomized inside the unit where the produced vapor is condensed on the inner surfaces of the unit outer walls to outside. The system was experimentally tested under different weather conditions. It is obtained that the system can produce about 9 l a day per quadratic meter of collector surface area. By that it can produce about 1.6 l/kWh of solar energy. Moreover, the water temperature has a great effect on the system performance although the scaling possibility is becoming significant. By that way the cost of a liter water production is relatively high and is obtained as 0.5 US$.

scholarly journals A Bit String Content Aware Chunking Strategy for Reduced CPU Energy on Cloud Storage

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bin Zhou ◽  
ShuDao Zhang ◽  
Ying Zhang ◽  
JiaHao Tan
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
System Performance ◽  
Cloud Storage ◽  
High Performance ◽  
Factors Affecting ◽  
Redundant Data ◽  
Energy Factors ◽  
The Cost ◽  
Content Aware

In order to achieve energy saving and reduce the total cost of ownership, green storage has become the first priority for data center. Detecting and deleting the redundant data are the key factors to the reduction of the energy consumption of CPU, while high performance stable chunking strategy provides the groundwork for detecting redundant data. The existing chunking algorithm greatly reduces the system performance when confronted with big data and it wastes a lot of energy. Factors affecting the chunking performance are analyzed and discussed in the paper and a new fingerprint signature calculation is implemented. Furthermore, a Bit String Content Aware Chunking Strategy (BCCS) is put forward. This strategy reduces the cost of signature computation in chunking process to improve the system performance and cuts down the energy consumption of the cloud storage data center. On the basis of relevant test scenarios and test data of this paper, the advantages of the chunking strategy are verified.

scholarly journals Performance analysis of retrofit R410a refrigerant with R32 refrigerant on a split air conditioner

2021 ◽  
Vol 2 (1) ◽  
pp. 1-4
Author(s):  
I Nengah Ardita ◽  
◽  
I Gusti Agung Bagus Wirajati ◽  
I Dewa Made Susila ◽  
Sudirman Sudirman ◽  
...  
Keyword(s):  
System Performance ◽  
Air Conditioning ◽  
Quantitative Description ◽  
Air Conditioner ◽  
Experimental Equipment ◽  
Evaporation Heat ◽  
Before And After ◽  
Common Group ◽  
The Cost ◽  
Design And Manufacture

Split air conditioning (AC) is the most widely used in the community for both commercial and domestic utilities. At the present refrigerant which used in Split AC is mostly common group of HFCs, such as R410a. R410a is a zeotropic refrigerant and if there is a leak in the system, it cannot be added this refrigerant. This will increase the cost of maintenance. The aims of this research is to investigate the retrofit of R410a with R32 on the Split AC system. The R32 is chosen because it has higher latent evaporation heat at the same temperature and has less effect on global warming. The refrigeration effect, the power consumption and the system performance are the main three quantities that want to be examined in this research which are observed before and after retrofit. Experimental investigation conducted during this research, including design and manufacture of experimental equipment, calibration and tools installment, collecting the experimental data and analysis by quantitative description method before and after retrofit. The results informed that cooling effect increased during the research, but the COP system has a slight decrease about 4%. R32 refrigerant is quite feasible as a retrofit refrigerant to R410a refrigerant.

A Buffer Pool Optimization Algorithm Based on Max-Heap

2014 ◽  
Vol 945-949 ◽  
pp. 2439-2442
Author(s):  
Xiao Mei Xiong
Keyword(s):  
Optimization Algorithm ◽  
Database Access ◽  
Buffer Pool ◽  
Operation Efficiency ◽  
Data Query ◽  
Recursive Sequence ◽  
Access Request

To reduce the number of requests to the database connection, this paper designed the max-heap in buffer pool. We use the buffer pool maintenance algorithm to manage SQL data query request when database access intensive. When we update the max-heap, the structure of the buffer pool will be updated and the heap will be balance through the heap of recursive sequence, it got good performance in database access request. Experiment results show that this algorithm can improve the operation efficiency of system effectively.

A decision-making method based on Bayesian optimization algorithm for small modular reactor

Kerntechnik ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. 109-121 ◽  
Author(s):  
B. Zhang ◽  
M. Peng ◽  
S. Cheng ◽  
L. Sun
Keyword(s):  
Decision Making ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimal Operation ◽  
Bayesian Optimization ◽  
Operating Personnel ◽  
Bayesian Optimization Algorithm ◽  
Autonomous Operation ◽  
Small Modular Reactor ◽  
The Cost

Abstract Small modular reactors (SMRs) are suitable for deployment in isolated underdeveloped areas to support highly localized microgrids. In order to achieve almost autonomous operation for reducing the cost of operating personnel, an autonomous control system with decision-making capability is needed. In this paper, a decision-making method based on Bayesian optimization algorithm (BOA) is proposed to explore the optimal operation scheme under fault conditions. BOA is used to adjust exploration strategy of operation scheme according to observations (operation schemes previously explored). To measure the feasibility of each operation scheme, an objective function that considers security and economy is established. BOA attempts to obtain the optimal operation scheme with maximum of the objective function in as few iterations as possible. To verify the proposed method, all main pump powered off fault is simulated by RELAP5 code. The optimal operation scheme of the fault is applied, the transient result shows that all key parameters are within safe limits and SMR is maintained at relatively high power, which means that BOA has the decision-making capability to get an optimal operation scheme on fault conditions.

scholarly journals Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

2019 ◽  
Vol 9 (22) ◽  
pp. 4893 ◽  
Author(s):  
Ivana Strumberger ◽  
Nebojsa Bacanin  ◽  
Milan Tuba ◽  
Eva Tuba
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
System Performance ◽  
Resource Scheduling ◽  
Whale Optimization Algorithm ◽  
Cloud System ◽  
Data Sets ◽  
Computing Paradigm ◽  
Whale Optimization

The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation–exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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