scholarly journals Decision Support based Resource Allocation for Cost Reduction in Cloud Storage using Big Data Analytics

2019 ◽  
Vol 8 (3) ◽  
pp. 8124-8126
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Data Storage ◽  
Input Data ◽  
Similarity Coefficient ◽  
Storage Efficiency ◽  
Decision Support Model ◽  
Storage Overhead ◽  
Tanimoto Similarity ◽  
And Storage

Provision of highly efficient storage for dynamically growing data is considered problem to be solved in data mining. Few research works have been designed for big data storage analytics. However, the storage efficiency using conventional techniques was not sufficient as where data duplication and storage overhead problem was not addressed. In order to overcome such limitations, Tanimoto Regressive Decision Support Based Blake2 Hashing Space Efficient Quotient Data Structure (TRDS-BHSEQDS) Model is proposed. Initially, TRDS-BHSEQDS technique gets larger number of input data as input. Then, TRDS-BHSEQDS technique computes 512 bits Blake2 hash value for each data to be stored. Consequently, TRDS-BHSEQDS technique applies Tanimoto Regressive Decision Support Model (TRDSM) where it carried outs regression analysis with application of Tanimoto similarity coefficient. During this process, proposed TRDS-BHSEQDS technique finds relationship between hash values of data by determining Tanimoto similarity coefficient value. If similarity value is ‘+1’, then TRDS-BHSEQDS technique considered that input data is already stored in BHSEQF memory. TRDSBHSEQDS technique enhances the storage efficiency of big data when compared to state-of-the-art works. The performance of TRDS-BHSEQDS technique is measured in terms of storage efficiency, time complexity and space complexity and storage overhead with respect to different numbers of input big data.

scholarly journals Intelligent Classification Method of Low Occupancy Big Data Based on Grid Index

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Haiyan Zhao ◽  
Shuangxi Li
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Load Balance ◽  
Virtual Machines ◽  
Classification Method ◽  
Storage Efficiency ◽  
Storage Process ◽  
Grid Index ◽  
Grid Application ◽  
Big Data Storage

In order to enhance the load balance in the big data storage process and improve the storage efficiency, an intelligent classification method of low occupancy big data based on grid index is studied. A low occupancy big data classification platform was built, the infrastructure layer was designed using grid technology, grid basic services were provided through grid system management nodes and grid public service nodes, and grid application services were provided using local resource servers and enterprise grid application services. Based on each server node in the infrastructure layer, the basic management layer provides load forecasting, image backup, and other functional services. The application interface layer includes the interfaces required for the connection between the platform and each server node, and the advanced access layer provides the human-computer interaction interface for the operation of the platform. Finally, based on the obtained main structure, the depth confidence network is constructed by stacking several RBM layers, the new samples are expanded by adding adjacent values to obtain the mean value, and the depth confidence network is used to classify them. The experimental results show that the load of different virtual machines in the low occupancy big data storage process is less than 40%, and the load of each virtual machine is basically the same, indicating that this method can enhance the load balance in the data storage process and improve the storage efficiency.

scholarly journals Efficiency of Flat File Database Approach in Data Storage and Data Extraction for Big Data

2018 ◽  
Vol 9 (2) ◽  
pp. 460
Author(s):  
Mohd Kamir Yusof ◽  
Mustafa Man
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Large Volume ◽  
Data Extraction ◽  
Flat File ◽  
Huge Data ◽  
Powerful Approach ◽  
Extensible Markup ◽  
Database Technology ◽  
And Storage

Big data is the latest industry buzzword to describe large volume of structured and unstructured data that can be difficult to process and analyze. Most of organization looking for the best approach to manage and analyze the large volume of data especially in making a decision. XML and JSON are chosen by many organization because of powerful approach during retrieval and storage processes. However, these approaches, the execution time for retrieving large volume of data are still considerably inefficient due to several factors. In this contribution, three databases approaches namely Extensible Markup Language (XML), Java Object Notation (JSON) and Flat File database approach were investigated to evaluate their suitability for handling thousands records of publication data. The results showed flat file is the best choice for query retrieving speed and CPU usage. These are essential to cope with the characteristics of publication’s data. Whilst, XML, JSON and Flat File database approach technologies are relatively new to date in comparison to the relational database. Indeed, Text File Format technology demonstrates greater potential to become a key database technology for handling huge data due to increase of data annually.

Chemometrics

2019 ◽  
Vol 2 (1) ◽  
pp. 1-42 ◽  
Author(s):  
Gerard G. Dumancas ◽  
Ghalib Bello ◽  
Jeff Hughes ◽  
Renita Murimi ◽  
Lakshmi Viswanath ◽  
...  
Keyword(s):  
Analytical Chemistry ◽  
Big Data ◽  
Data Storage ◽  
State Of The Art ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Computer Applications ◽  
Analytical Instruments ◽  
Big Data Visualization ◽  
And Storage

The accumulation of data from various instrumental analytical instruments has paved a way for the application of chemometrics. Challenges, however, exist in processing, analyzing, visualizing, and storing these data. Chemometrics is a relatively young area of analytical chemistry that involves the use of statistics and computer applications in chemistry. This article will discuss various computational and storage tools of big data analytics within the context of analytical chemistry with examples, applications, and usage details in relation to fog computing. The future of fog computing in chemometrics will also be discussed. The article will dedicate particular emphasis to preprocessing techniques, statistical and machine learning methodology for data mining and analysis, tools for big data visualization, and state-of-the-art applications for data storage using fog computing.

scholarly journals Decision Support System in a Memristor-Based Mobile CIM Architecture Applied on Big Data Computation and Storage

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wei Li ◽  
Zhao Deng
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Data Processing ◽  
Computer Architecture ◽  
Support System ◽  
Integrated Circuit ◽  
Data Communication ◽  
Oxide Semiconductor ◽  
And Storage

Data computation and storage are essential parts of developing big data applications. The memristor device technology could remove the speed and energy efficiency bottleneck in the existing data processing. The present experimental work investigates the decision support system in a new architecture, computation-in-memory (CIM) architecture, which can be utilized to store and process big data in the same physical location at a faster rate. The decision support system is used for data computation and storage, with the aims of helping memory units read, write, and erase data and supporting their decisions under big data communication ambiguities. Data communication is realized within the crossbar by the support of peripheral controller blocks. The feasibility of the CIM architecture, adaptive read, write, and erase methods, and memory accuracy were investigated. The integrated circuit emphasis (SPICE) simulation results show that the proposed CIM architecture has the potential of improving the computing efficiency, energy consumption, and performance area by at least two orders of magnitude. CIM architecture may be used to mitigate big data processing limits caused by the conventional computer architecture and complementary metal-oxide-semiconductor (CMOS) transistor process technologies.

A Decision Support Tool for Evaluating Big Data Investment in Transport: Empirical evidence from European use cases of NOESIS project

2020 ◽  
Author(s):  
Christos Katrakazas ◽  
Natalia Sobrino ◽  
Ilias Trochidis ◽  
Jose Manuel Vassallo ◽  
Stratos Arampatzis ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Empirical Evidence ◽  
Decision Support Tool ◽  
Use Cases ◽  
Support Tool
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