scholarly journals Big data theory based spectrum sensing algorithm for the satellite cognitive radio network

2021 ◽  
Author(s):  
Mingchuan Yang ◽  
Xinye Shao ◽  
Guanchang Xue ◽  
Bingyu Xie
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Large Scale ◽  
Radio Spectrum ◽  
Big Data Analysis ◽  
New Approach ◽  
Analysis Theory ◽  
Sample Covariance

AbstractIn order to deal with the difficulty of spectrum sensing in cognitive satellite wireless networks, a large-scale cognitive network spectrum sensing algorithm based on big data analysis theory is studied, and a new algorithm using mean exponential eigenvalue is proposed. This new approach fully uses all the eigenvalues in sample covariance matrix of the sensing results to make the decision, which can effectively improve the detection performance without obtaining the prior information from licensed users. Through simulation, the performance of various large scale cognitive radio spectrum sensing algorithms based on big data analysis theory is compared, and the influence of satellite to ground channel conditions and the number of sensing nodes on the performance of the algorithm is discussed.

Big Data Analysis with Hadoop on Personalized Incentive Model with Statistical Hotel Customer Data

2016 ◽  
Vol 4 (3) ◽  
pp. 1-21 ◽  
Author(s):  
Sungchul Lee ◽  
Eunmin Hwang ◽  
Ju-Yeon Jo ◽  
Yoohwan Kim
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Data Sets ◽  
New Approach ◽  
Customer Data ◽  
Customer Information ◽  
Large Scale Data ◽  
Incentive Model

Due to the advancement of Information Technology (IT), the hospitality industry is seeing a great value in gathering various kinds of and a large amount of customers' data. However, many hotels are facing a challenge in analyzing customer data and using it as an effective tool to understand the hospitality customers better and, ultimately, to increase the revenue. The authors' research attempts to resolve the current challenges of analyzing customer data in hospitality by utilizing the big data analysis tools, especially Hadoop and R. Hadoop is a framework for processing large-scale data. With the integration of new approach, their study demonstrates the ways of aggregating and analyzing the hospitality customer data to find meaningful customer information. Multiple decision trees are constructed from the customer data sets with the intention of classifying customers' needs and customers' clusters. By analyzing the customer data, the study suggests three strategies to increase the total expenditure of the customers within a limited amount of time during their stay.

scholarly journals Erratum to: ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Jiangming Sun ◽  
Nina Jeliazkova ◽  
Vladimir Chupakhin ◽  
Jose-Felipe Golib-Dzib ◽  
Ola Engkvist ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Large Scale Dataset

scholarly journals Influencing Factors of e-Commerce Enterprise Development Based on Mobile Computing Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yixue Zhu ◽  
Boyue Chai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Big Data Analysis ◽  
Support Vector ◽  
Data Sets ◽  
Large Scale Data ◽  
Vector Machines ◽  
Physical Information ◽  
Scale Data

With the development of increasingly advanced information technology and electronic technology, especially with regard to physical information systems, cloud computing systems, and social services, big data will be widely visible, creating benefits for people and at the same time facing huge challenges. In addition, with the advent of the era of big data, the scale of data sets is getting larger and larger. Traditional data analysis methods can no longer solve the problem of large-scale data sets, and the hidden information behind big data is digging out, especially in the field of e-commerce. We have become a key factor in competition among enterprises. We use a support vector machine method based on parallel computing to analyze the data. First, the training samples are divided into several working subsets through the SOM self-organizing neural network classification method. Compared with the ever-increasing progress of information technology and electronic equipment, especially the related physical information system finally merges the training results of each working set, so as to quickly deal with the problem of massive data prediction and analysis. This paper proposes that big data has the flexibility of expansion and quality assessment system, so it is meaningful to replace the double-sidedness of quality assessment with big data. Finally, considering the excellent performance of parallel support vector machines in data mining and analysis, we apply this method to the big data analysis of e-commerce. The research results show that parallel support vector machines can solve the problem of processing large-scale data sets. The emergence of data dirty problems has increased the effective rate by at least 70%.

Towards easily accessible interactive big-data analysis on supercomputers

2020 ◽  
Author(s):  
Katharina Höflich ◽  
Martin Claus ◽  
Willi Rath ◽  
Dorian Krause ◽  
Benedikt von St. Vieth ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
High Performance ◽  
Large Scale ◽  
Big Data Analysis ◽  
Ocean Science ◽  
Working Environments ◽  
Interactive Data Analysis ◽  
Science Community ◽  
Interactive Data

<p>Demand on high-end high performance computer (HPC) systems by the Earth system science community today encompasses not only the handling of complex simulations but also machine and deep learning as well as interactive data analysis workloads on large volumes of data. This poster addresses the infrastructure needs of large-scale interactive data analysis workloads on supercomputers. It lays out how to enable optimizations of existing infrastructure with respect to accessibility, usability and interactivity and aims at informing decision making about future systems. To enhance accessibility, options for distributed access, e.g. through JupyterHub, will be evaluated. To increase usability, the unification of working environments via the operation and the joint maintenance of containers will be explored. Containers serve as a portable base software setting for data analysis application stacks and allow for long-term usability of individual working environments and repeatability of scientific analysis. Aiming for interactive big-data analysis on HPC will also help the scientific community in utilizing increasingly heterogeneous supercomputers, since the modular data-analysis stack already contains solutions for seamless use of various architectures such as accelerators. However, to enable day-to-day interactive work on supercomputers, the inter-operation of workloads with quick turn-around times and highly variable resource demands needs to be understood and evaluated. To this end, scheduling policies on selected HPC systems are reviewed with respect to existing technical solutions such as job preemption, utilizing the resiliency features of parallel computing toolkits like Dask. Presented are preliminary results focussing on the aspects of usability and interactive use of HPC systems on the basis of typical use cases from the ocean science community.</p>

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