RETRACTED CHAPTER: A Cooperative Placement Method for Machine Learning Workflows and Meteorological Big Data Security Protection in Cloud Computing

2019 ◽  
pp. 94-111
Author(s):  
Xinzhao Jiang ◽  
Wei Kong ◽  
Xin Jin ◽  
Jian Shen
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Data Security ◽  
Placement Method

Cryptography in Big Data Security

2018 ◽  
pp. 71-94
Author(s):  
Navin Jambhekar ◽  
Chitra Anil Dhawale
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Information Security ◽  
Data Security ◽  
Computing System ◽  
Mobile Client ◽  
Security Issues ◽  
Cloud Computing System ◽  
Cloud Server ◽  
Encrypted Search

Information security is a prime goal for every individual and organization. The travelling from client to cloud server can be prone to security issues. The big data storages are available through cloud computing system to facilitate mobile client. The information security can be provided to mobile client and cloud technology with the help of integrated parallel and distributed encryption and decryption mechanism. The traditional technologies include the plaintext stored across cloud and can be prone to security issues. The solution provided by applying the encrypted data upload and encrypted search. The clouds can work in collaboration; therefore, the encryption can also be done in collaboration. Some part of encryption handle by client and other part handled by cloud system. This chapter presents the security scenario of different security algorithms and the concept of mobile and cloud computing. This chapter precisely defines the security features of existing cloud and big data system and provides the new framework that helps to improve the data security over cloud computing and big data security system.

scholarly journals A Supervised Learning Algorithm to Forecast Weather Conditions for Playing Cricket

2019 ◽  
Vol 9 (1) ◽  
pp. 1560-1565
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Weather Conditions ◽  
Redundant Data ◽  
Classification Technique

Now days, Machine learning is considered as the key technique in the field of technologies, such as, Internet of things (IOT), Cloud computing, Big data and Artificial Intelligence etc. As technology enhances, lots of incorrect and redundant data are collected from these fields. To make use of these data for a meaningful purpose, we have to apply mining or classification technique in the real world. In this paper, we have proposed two nobel approaches towards data classification by using supervised learning algorithm

scholarly journals MetaCloudDataStorage Architecture for Big Data Security in Cloud Computing

2016 ◽  
Vol 87 ◽  
pp. 128-133 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Chandu Thota ◽  
M. Vijay Kumar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Security

Data Science: A New Paradigm in the Age of Big-Data Science and Analytics

2017 ◽  
Vol 13 (02) ◽  
pp. 119-143 ◽  
Author(s):  
Claude E. Concolato ◽  
Li M. Chen
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Computer Science ◽  
Data Science ◽  
Big Data Analytics ◽  
Modern World ◽  
Growth Data ◽  
New Paradigm ◽  
Great Opportunity

As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.

scholarly journals Research on Big data Security privacy Protection based on cloud Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042077
Author(s):  
Tongtong Xu ◽  
Lei Shi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Security ◽  
Privacy Protection ◽  
Data Privacy ◽  
Cloud Services ◽  
Security Mechanism ◽  
Computing Platform ◽  
Cloud Servers

Abstract Cloud computing is a new way of computing and storage. Users do not need to master professional skills, but can enjoy convenient network services as long as they pay according to their own needs. When we use cloud services, we need to upload data to cloud servers. As the cloud is an open environment, it is easy for attackers to use cloud computing to conduct excessive computational analysis on big data, which is bound to infringe on others’ privacy. In this process, we inevitably face the challenge of data security. How to ensure data privacy security in the cloud environment has become an urgent problem to be solved. This paper studies the big data security privacy protection based on cloud computing platform. This paper starts from two aspects: implicit security mechanism and display security mechanism (encryption mechanism), so as to protect the security privacy of cloud big data platform in data storage and data computing processing.

scholarly journals MAPPING GLACIER CHANGES USING CLUSTERING TECHNIQUES ON CLOUD COMPUTING INFRASTRUCTURE

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 29-34
Author(s):  
V. Ayma ◽  
C. Beltrán ◽  
P. N. Happ ◽  
G. A. O. P. Costa ◽  
R. Q. Feitosa
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Machine Learning Algorithms ◽  
Local Mode ◽  
Ground Truth Data ◽  
Computational Performance ◽  
Thematic Accuracy ◽  
Computing Infrastructure

<p><strong>Abstract.</strong> Climate change and its effects are taking more importance nowadays; and glaciers are one of the most affected ecosystems by that, considering that the energy of Earth’s surface and its temperature may be directly related to glacier temporal changes. Then, the comprehension of glaciers behaviour, by its retreating or melting critical conditions, can be achieved by the analysis of Remote Sensing data, but considering the unprecedented volumes of information currently provided by satellites sensors, we can refer to this analysis as a big data problem. Machine learning techniques have the potential to improve the analysis of this type of data; however, most current machine learning algorithms are unable to properly process such huge volumes of data. In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means and Expectation Maximization algorithms, as distributed clustering solutions, exploiting the capabilities of cloud computing infrastructure for processing very large datasets. The solution was developed over the InterCloud Data Mining Package, which is a suite of distributed classification methods, previously employed in hyperspectral image analysis. In this work we extended the functionalities of that package, by making it able to process multispectral images using the aforementioned clustering algorithms. To validate our proposal, we analysed the Ausangate glacier, located on the Andes Mountains, in Peru, by mapping the changes in such environment through a multi-temporal Remote Sensing analysis. Our results and conclusions are focused on the thematic accuracy and the computational performance achieved by our proposed solution. Thematic accuracy was assessed by comparing the automatically detected glacier areas by the clustering approaches against the manually selected ground truth data. We compared the computational load involved in executing the clustering processes sequentially and in a distributed fashion, using a local mode and cluster configuration over a cloud computing infrastructure.</p>

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