scholarly journals Assessment of Benefits and Disadvantages of Implementing Cloud-Specific Solutions in Polish Companies on the Example of ERP Systems

2020 ◽  
Author(s):  
Remigiusz Zieliński ◽  
Sebastian Kot ◽  
Katarzyna Zielińska
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Operational Efficiency ◽  
Erp Systems ◽  
Cloud Data ◽  
It Systems ◽  
Complex Process ◽  
Questionnaire Surveys ◽  
Cloud Data Processing ◽  
Theoretical Considerations

Abstract Both dynamics and environmental turbulences result in constant updating of IT and communication specific technologies utilized within the scope of various organizations. The Polish IT market is characterized by a dynamic development of the cloud data processing model that offers access to various resources via the network in the form of convenient services. The transfer of IT systems (including ERP) to the cloud is a complex process and while the connection can be made, it may be met with a number of problems. The predominant aim of the article, aside from theoretical considerations, is to showcase the possibilities of supporting a company’s operational schemes by means of IT solutions available within the cloud; as well as to indicate both the benefits and the difficulties Polish companies have to face while implementing cloud computing specific solutions. Within the scope of the article, selected outcomes of questionnaire surveys are going to be presented. Research results have shown that IT solutions and ERP systems available in the cloud have been positively perceived by the respondents, especially with regard to improving the overall operational efficiency of companies.

Cloud Data Management Based on Cloud Computing Technology

2014 ◽  
Vol 543-547 ◽  
pp. 3573-3576
Author(s):  
Yuan Jun Zou
Keyword(s):  
Cloud Computing ◽  
Parallel Computing ◽  
Data Processing ◽  
Development Planning ◽  
Computer Data ◽  
Cloud Data ◽  
Computing Platform ◽  
Convergence Error ◽  
Cloud Computing Platform ◽  
Cloud Data Processing

Cloud computing, networking and other high-end computer data processing technology are the important contents of eleven-five development planning in China. They have developed rapidly in recent years in the field of engineering. In this paper, we combine parallel computing with the collaborative simulation principle, design a cloud computing platform, establish the mathematical model of cloud data processing and parallel computing algorithm, and verify the applicability of algorithm through the numerical simulation. Through numerical calculation, cloud computing platform can be divided into complex grids, and the transmission speed is fast, which is eight times than the finite difference method. The mesh is meticulous, which reaches millions. Convergence error is minimum, only 0.001. The calculation accuracy is up to 98.36%.

The Research of Point Cloud Data Processing Technology

2014 ◽  
Vol 628 ◽  
pp. 426-431
Author(s):  
Li Bo Zhou ◽  
Fu Lin Xu ◽  
Su Hua Liu
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Quality Data ◽  
Surface Generation ◽  
Point Cloud Data ◽  
Data Smoothing ◽  
Cloud Data ◽  
Data Point ◽  
Cloud Data Processing

Data processing is a key to reverse engineering, the results of which will directly affect the quality of the model reconstruction. Eliminate noise points are the first step in data processing, The method of using Coons surface to determine the noise in the data point is proposed. To reduce the amount of calculation and improve the surface generation efficiency, data point is reduced. According to the surrounding point coordinate information, the defect coordinates are interpolated. Data smoothing can improve the surface generation quality, data block can simplify the creation of the surface. Auto parts point cloud data is processed, and achieve the desired effect.

Point Cloud Data Processing Method of Cavity 3D Laser Scanner

2013 ◽  
Vol 33 (8) ◽  
pp. 0812003 ◽  
Author(s):  
陈凯 Chen Kai ◽  
张达 Zhang Da ◽  
张元生 Zhang Yuansheng
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Laser Scanner ◽  
Processing Method ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Processing Method ◽  
Cloud Data Processing

Security Issues in Fog Computing and ML-Based Solutions

2021 ◽  
pp. 209-234
Author(s):  
Himanshu Sahu ◽  
Gaytri
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Processing ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Special Focus ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Security Issues ◽  
Learning Techniques

IoT requires data processing, which is provided by the cloud and fog computing. Fog computing shifts centralized data processing from the cloud data center to the edge, thereby supporting faster response due to reduced communication latencies. Its distributed architecture raises security and privacy issues; some are inherited from the cloud, IoT, and network whereas others are unique. Securing fog computing is equally important as securing cloud computing and IoT infrastructure. Security solutions used for cloud computing and IoT are similar but are not directly applicable in fog scenarios. Machine learning techniques are useful in security such as anomaly detection, intrusion detection, etc. So, to provide a systematic study, the chapter will cover fog computing architecture, parallel technologies, security requirements attacks, and security solutions with a special focus on machine learning techniques.

Flexible Access Control over Privacy-Preserving Cloud Data Processing

2020 ◽  
pp. 231-255
Author(s):  
Wenxiu Ding ◽  
Xinren Qian ◽  
Rui Hu ◽  
Zheng Yan ◽  
Robert H. Deng
Keyword(s):  
Access Control ◽  
Data Processing ◽  
Privacy Preserving ◽  
Cloud Data ◽  
Cloud Data Processing

Triangulation-Based Point Cloud Data Processing Technology Research and Application

2014 ◽  
Vol 610 ◽  
pp. 729-733
Author(s):  
Ke He Wu ◽  
Wen Chao Cui ◽  
Bo Hao Cheng ◽  
Qian Yuan Zhang
Keyword(s):  
Data Processing ◽  
Data Storage ◽  
Point Cloud ◽  
Extraction Process ◽  
Programming Algorithm ◽  
Point Cloud Data ◽  
Boundary Extraction ◽  
Processing Technologies ◽  
Cloud Data ◽  
Cloud Data Processing

With the "Digital Earth" concept being put forward, people are starting to focus on geospatial information technology. Traditional manual building modeling process is gradually eliminated by history due to cumbersome and inefficient work. With massive data storage and processing technologies emerging and improving, people begin to explore building point cloud data measured by laser radar technology and to use point cloud data processing software for further building boundary extraction. In the model boundary extraction process, the use of prototype with the model fit is a good, clear and easy programming algorithm and triangulation algorithm.

scholarly journals CNN based lane detection with instance segmentation in edge-cloud computing

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Wei Wang ◽  
Hui Lin ◽  
Junshu Wang
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Autonomous Driving ◽  
Lane Detection ◽  
Dual Model ◽  
High Definition ◽  
Processing Efficiency ◽  
Cloud Data ◽  
Lane Recognition ◽  
Instance Segmentation

Abstract At present, the number of vehicle owners is increasing, and the cars with autonomous driving functions have attracted more and more attention. The lane detection combined with cloud computing can effectively solve the drawbacks of traditional lane detection relying on feature extraction and high definition, but it also faces the problem of excessive calculation. At the same time, cloud data processing combined with edge computing can effectively reduce the computing load of the central nodes. The traditional lane detection method is improved, and the current popular convolutional neural network (CNN) is used to build a dual model based on instance segmentation. In the image acquisition and processing processes, the distributed computing architecture provided by edge-cloud computing is used to improve data processing efficiency. The lane fitting process generates a variable matrix to achieve effective detection in the scenario of slope change, which improves the real-time performance of lane detection. The method proposed in this paper has achieved good recognition results for lanes in different scenarios, and the lane recognition efficiency is much better than other lane recognition models.

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