A Review on Amazon Web Service (AWS), Microsoft Azure &amp; Google Cloud Platform (GCP) Services

Gupta, Bulbul; Mittal, Pooja; Mufti, Tabish

doi:10.4108/eai.27-2-2020.2303255

Cited by 36 publications

(16 citation statements)

References 11 publications

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“…The platform rewrites the execution of the underlying web service code during execution, so that the application calling the web service can automatically perform distributed computing. As long as the network application is created on the platform, it can easily perform distributed data computing and storage, so it has certain scalability [14]. The literature deeply analyzes the advantages and disadvantages of SVM algorithm and KNN algorithm.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Application and functional simulation of data mining technology in Hadoop cloud platform based on improved algorithm

Dai¹

2023

Soft Comput

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Data mining algorithms can process target data and extract useful hidden information, which is helpful for decision making. However, current mining algorithms have some shortcomings such as time-consuming processing of big data or inability to process massive data. Since data mining technology cannot be used in the traditional cloud platform environment, it is necessary to improve the algorithm to make it more adaptable to the cloud platform environment. By analyzing the actual application process of BP classification algorithm, this paper describes the practicability of BP classification algorithm, analyzes the process of data mining based on Hadoop cloud platform, and explains the development concept of BP classification algorithm. The source of data mining algorithm supported by cloud computing is discussed. Finally, based on the data mining system of Hadoop cloud platform, this paper designs the corresponding system architecture and data interface, and establishes a suitable testing environment for this system, and completes the simulation experiment test by design.It can be SEEN from the research results that the computation time of this algorithm is directly proportional to the amount of data, and it shows a linear relationship.Compared with the traditional data mining algorithm, the optimized BP algorithm in this paper can significantly save resources in terms of spatial features. This paper designs a kind of optimized operation system based on Hadoop platform through comprehensive analysis of data mining technology and improved algorithm, so as to promote the comprehensive development of data mining technology.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Application and functional simulation of data mining technology in Hadoop cloud platform based on improved algorithm

Dai¹

2023

Soft Comput

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“…To evaluate the proposed method, we developed a prototype that we deployed on Amazon Web Service (AWS), Google Cloud Platform, and Microsoft Azure. 36 Table 1 showcases the results of the experimental evaluation conducted to demonstrate the efficiency and reliability of the algorithm in terms of uploading and downloading times. The data storage schema employed in the evaluations offers remarkable flexibility, enabling us to compare different solutions across various cloud services, namely AWS, Google Cloud Platform, and Microsoft Azure.…”

Section: Discussionmentioning

confidence: 99%

“…To evaluate the proposed method, we developed a prototype that we deployed on Amazon Web Service (AWS), Google Cloud Platform, and Microsoft Azure 36 . Table 1 showcases the results of the experimental evaluation conducted to demonstrate the efficiency and reliability of the algorithm in terms of uploading and downloading times.…”

Section: Discussionmentioning

confidence: 99%

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

Mellone,

De Vita,

Sánchez‐Gallegos

et al. 2023

Concurrency and Computation

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SummaryCloud‐based services have proved useful in several research fields, such as engineering, health science, and astrophysics, to mention a few examples. The computational environmental science community developed a strong need for cloud facilities to store, process, and manage data from observations and numerical models for simulations and forecasts. Weather forecast models and global sensor networks deal with multidimensional geo‐referenced data∖sets. However, environmental data consumer applications usually require a relatively small amount of multidimensional input data slice to analyze a specific area or time interval. Hence, reducing data dimension for information retrieval is mandatory. This paper presents a twofold solution: a technique to load and retrieve the sliced multidimensional data set on different cloud services such as Amazon Web Service (AWS), Google Cloud Platform, and Microsoft Azure. The experimental results performed on these cloud services highlight that the proposed method can significantly speed up the process of loading and retrieving the data slices compared to working with the entire data set in bulk or OPeNDAP server.

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“…Apache Hadoop [14] [15] [16], RapidMiner [17] [18] [19], and Microsoft Azure [20] [21] [22] [23]. Aside from data utilization, data privacy [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] must also be considered when datasets are allowedly utilized in big data analytics.…”

Section: Introductionmentioning

confidence: 99%

Aggregate Query Frameworks for Preserving Privacy Data in Big Data Analytics

Riyana

Sasujit

Homdoung

2023

Preprint

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Data utility and data privacy are both serious issues that must be considered when datasets are released to use in big data analytics because they are traded-off issues. That is, high-utility datasets are generally high risks in terms of privacy violation issues. Likewise, datasets are formed to be high security in terms of privacy preservation, they often lead to data utility issues. To address these issues in datasets, several privacy preservation models have been proposed such as k-Anonymity, l-Diversity, t-Closeness, Anatomy, k-Likeness, and (lp1, . . . , lpn)-Privacy, i.e., all users’ explicit identifier values and all unique quasi-identifier values in datasets are removed and distorted respectively. Unfortunately, these privacy preservation models are static data models and still have data utility issues that must be addressed. Thus, they could be insufficient to address privacy violation issues in big data analytics. For this reason, a new efficient and effective privacy preservation model is proposed in this work such that it is based on aggregate query answers that can guarantee the confidence of the range and the minimized number of values that can be re-identified. The aims of the proposed privacy preservation model are that aside from privacy preservation constraints, the complexity and data utility are also maintained as much as possible. Furthermore, we show that the proposed model is a privacy preservation model that is efficient and effective by using extensive experiments.

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A Review on Amazon Web Service (AWS), Microsoft Azure & Google Cloud Platform (GCP) Services

Cited by 36 publications

References 11 publications

RETRACTED ARTICLE: Application and functional simulation of data mining technology in Hadoop cloud platform based on improved algorithm

RETRACTED ARTICLE: Application and functional simulation of data mining technology in Hadoop cloud platform based on improved algorithm

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

Aggregate Query Frameworks for Preserving Privacy Data in Big Data Analytics

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