Big Data Cloud Computing Research Articles

Big Data Enabled Mobile Network Design for 5G and Beyond

Mobile communication networks are more and more characterized by the integration of distributed and centralized computing and storage resources. Big data capability thus available throughout such networks will not only deliver enhanced system performance, but also profoundly impact the design and standardization of the next-generation network architecture, protocol stack, signaling procedure, and physical- layer processing. In this article, a mobile network architecture enabled by big data analytics is proposed, which is capable of efficient resource orchestration, content distribution, and radio access network optimization. The protocol stack configuration at each access point and the processing optimization of each layer are presented. Key physical layer designs including reference signals and frame structure are discussed. Moreover, utilizing signals in the transform domains, such as delay, Doppler, and angle, may bring enlarged coherence time of the effective channels. It enables much simpler physical layer design, and effectively bridges the latency gap between big data cloud computing and real-time network optimization.

Data Reconstruction Through Sequence Based Mapping in Secured Data Partitioning

Recent researches have proposed data partitioning technique with secret sharing to enhance the security in cloud computing. However, its complexity in reconstructing while preserving confidentiality has a limitation of practical use, specifically when it involves a large amount of data. In this paper, we explored the existing mapping technique called partition based indexing that is being used to reconstruct the shares. Nevertheless, we found that its efficiency has decreased when the amount of data increased. Thus, this has motivated us to propose a sequence based mapping to increase the efficiency of data reconstruction in secured data partitioning with secret sharing. The proposed technique has been evaluated through a series of simulation using 10000 data. The performance was evaluated based on the time taken to achieve data reconstruction for different number of shares. As a result, we proved that our proposal, which is named as a sequence based mapping technique has successfully improved more than 40 percent of the performance of data reconstruction compared to indexing technique. As such, we conclude that our proposal on sequence based mapping is an ideal technique for improving performance of data reconstruction in data-partitioning with secret sharing and preserving confidentiality of big data in cloud computing.

Intercrossed Access Controls for Secure Financial Services on Multimedia Big Data in Cloud Systems

The dramatically growing demand of Cyber Physical and Social Computing (CPSC) has enabled a variety of novel channels to reach services in the financial industry. Combining cloud systems with multimedia big data is a novel approach for Financial Service Institutions (FSIs) to diversify service offerings in an efficient manner. However, the security issue is still a great issue in which the service availability often conflicts with the security constraints when the service media channels are varied. This paper focuses on this problem and proposes a novel approach using the Semantic-Based Access Control (SBAC) techniques for acquiring secure financial services on multimedia big data in cloud computing. The proposed approach is entitled IntercroSsed Secure Big Multimedia Model (2SBM), which is designed to secure accesses between various media through the multiple cloud platforms. The main algorithms supporting the proposed model include the Ontology-Based Access Recognition (OBAR) Algorithm and the Semantic Information Matching (SIM) Algorithm . We implement an experimental evaluation to prove the correctness and adoptability of our proposed scheme.

Operational climate prediction in the era of big data in China: Reviews and prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.

MetaCloudDataStorage Architecture for Big Data Security in Cloud Computing

The cloud is increasingly being used to store and process the big data. Many researchers have been trying to protect big data in cloud computing environment. Traditional security mechanisms using encryption are neither efficient nor suited to the task of protecting big data in the Cloud. In this paper, we first discuss about challenges and potential solutions for protecting big data in cloud computing. Second, we proposed MetaCloudDataStorage Architecture for protecting Big Data in Cloud Computing Environment. This framework ensures that efficient processing of big data in cloud computing environment and gains more business insights.

Research on the Performance Optimization of Hadoop in Big Data Environment

In the age of Internet, the data transmission and storage got rapid progress, however, data processing and information extraction is still exist many problems to solve. Under the condition of so much data, processing data, get useful information; In cloud computing, big data environment to adopt the method of distributed computing, such a large complex networks, however, requires a simulation environment, for comparison and optimization platform, it can save development costs. Hadoop can evaluate the performance of distributed cloud computing platform, so the Hadoop performance directly affects the evaluation on the performance of the big data cloud computing, which fully show the importance of performance of Hadoop. Algorithm is improved based on Hadoop platform, using the particle swarm optimization algorithm improved the calculation and implementation of the Hadoop platform, so as to improve its ability to execute and compute, the calculation results and analysis show that the proposed scheme is effective.

A survey on indexing techniques for big data: taxonomy and performance evaluation

The explosive growth in volume, velocity, and diversity of data produced by mobile devices and cloud applications has contributed to the abundance of data or `big data.' Available solutions for efficient data storage and management cannot fulfill the needs of such heterogeneous data where the amount of data is continuously increasing. For efficient retrieval and management, existing indexing solutions become inefficient with the rapidly growing index size and seek time and an optimized index scheme is required for big data. Regarding real-world applications, the indexing issue with big data in cloud computing is widespread in healthcare, enterprises, scientific experiments, and social networks. To date, diverse soft computing, machine learning, and other techniques in terms of artificial intelligence have been utilized to satisfy the indexing requirements, yet in the literature, there is no reported state-of-the-art survey investigating the performance and consequences of techniques for solving indexing in big data issues as they enter cloud computing. The objective of this paper is to investigate and examine the existing indexing techniques for big data. Taxonomy of indexing techniques is developed to provide insight to enable researchers understand and select a technique as a basis to design an indexing mechanism with reduced time and space consumption for BD-MCC. In this study, 48 indexing techniques have been studied and compared based on 60 articles related to the topic. The indexing techniques' performance is analyzed based on their characteristics and big data indexing requirements. The main contribution of this study is taxonomy of categorized indexing techniques based on their method. The categories are non-artificial intelligence, artificial intelligence, and collaborative artificial intelligence indexing methods. In addition, the significance of different procedures and performance is analyzed, besides limitations of each technique. In conclusion, several key future research topics with potential to accelerate the progress and deployment of artificial intelligence-based cooperative indexing in BD-MCC are elaborated on.

Study on the Optimization of Data Mining in Big Data

This paper proposes an analysis measure for Big Data by optimizing traditional data mining, base on Weka data analyzing platform ,K-means algorithm is employed through the interface programs in Weka system, so that optimized data mining techniques can be applied in cloud storage, cloud computing of Big Data by clustering analysis base on Big Data pre-processing and real-time monitoring of memory.

Security Issues Associated with Big Data in Cloud Computing

In this paper, we discuss security issues for cloud computing, Big data, Map Reduce and Hadoop environment. The main focus is on security issues in cloud computing that are associated with big data. Big data applications are a great benefit to organizations, business, companies and many large scale and small scale industries.We also discuss various possible solutions for the issues in cloud computing security and Hadoop. Cloud computing security is developing at a rapid pace which includes computer security, network security, information security, and data privacy. Cloud computing plays a very vital role in protecting data, applications and the related infrastructure with the help of policies, technologies, controls, and big data tools . Moreover, cloud computing, big data and its applications, advantages are likely to represent the most promising new frontiers in science.

Managing big data in cloud computing and its impact on E-learning in university

Managing big data in cloud computing and its impact on E-learning in university