A Preliminary Analysis Model of Big Data for Prevention of Bioaccumulation of Heavy Metal-Based Pollutants: Focusing on the Atmospheric Data Analyses

In order to meet external regulation and challenges, and improve the quality of internal economic activity analysis, this study establishes a linkage analysis system from corporate strategy to strategic objectives to financial indicators to business indicators by building 3 independent and interrelated analysis models. One of them is the model of influencing factors of change of operating efficiency index, one of them is the traceability analysis model of the sales of electricity and electricity price, and the last one is an investment performance traceability analysis model. In this study, the actual data of a unit is used as an example. With the help of big data analysis, we fully tap the value of the company’s big data, accurately locate the weak links and risk points of management. By doing this we finely promote economic activity analysis system more comprehensive, more real-time, more dynamic and more intelligent, and thus improve the efficiency of business decision-making. The practicality of economic activity analysis based on “operation, value and performance” is confirmed.

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Performance analysis model for big data applications in cloud computing

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-014-0019-z ◽

2014 ◽

Vol 3 (1) ◽

Cited By ~ 8

Author(s):

Luis Eduardo Bautista Villalpando ◽

Alain April ◽

Alain Abran

Keyword(s):

Cloud Computing ◽

Big Data ◽

Performance Analysis ◽

Analysis Model ◽

Big Data Applications

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Exploring Support Vector Machines for Big Data Analyses

10.1145/3494885.3494891 ◽

2021 ◽

Author(s):

Siyang Lu ◽

Yihong Chen ◽

Xiaolin Zhu ◽

Ziyi Wang ◽

Yangjun Ou ◽

...

Keyword(s):

Big Data ◽

Support Vector Machines ◽

Support Vector ◽

Data Analyses ◽

Vector Machines

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Application of mathematical probabilistic statistical model of base – FFCA financial data processing

Applied Mathematics and Nonlinear Sciences ◽

10.2478/amns.2021.1.00053 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Zhengqing Li ◽

Jiliang Mu ◽

Mohammed Basheri ◽

Hafnida Hasan

Keyword(s):

Big Data ◽

Statistical Model ◽

Probability Density ◽

Mathematical Reasoning ◽

Statistical Design ◽

Financial Data ◽

Data Filtering ◽

Analysis Model ◽

Design Data ◽

Mathematical Probability

Abstract In order to improve the detection and filtering ability for financial data, a data-filtering method based on mathematical probability statistical model, a descriptive statistical analysis model of big data filtering, probability density characteristic statistical design data filtering analysis combined with fuzzy mathematical reasoning, regression analysis according to probability density of financial data distribution, and threshold test and threshold judgment are conducted to realize data filtering. The test results show that the big data filtering and the reliability and convergence of the mathematical model are optimal.

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Research on the Construction of Big Data Classification System Based on Distributed Data Flow

Journal of Physics Conference Series ◽

10.1088/1742-6596/2136/1/012057 ◽

2021 ◽

Vol 2136 (1) ◽

pp. 012057

Author(s):

Han Zhou

Keyword(s):

Big Data ◽

New Technologies ◽

Data Classification ◽

Classification Model ◽

Distributed Data ◽

Analysis Model ◽

Database Construction ◽

Big Data Classification ◽

Operation Status ◽

Model Algorithm

Abstract In the context of the comprehensive popularization of network technical services and database construction system, more and more data are used by enterprises or individuals. It is difficult for the existing technology to meet the technical analysis requirements of the development of the era of big data. Therefore, in the development of practice, we should continue to explore new technologies and methods to reasonably use big data. Therefore, on the basis of understanding the current big data technology and its system operation status, this paper designs relevant algorithms according to the big data classification model, and verifies the effectiveness of the analysis model algorithm based on practice.

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Hadoop Performance Analysis Model with Deep Data Locality

Information ◽

10.3390/info10070222 ◽

2019 ◽

Vol 10 (7) ◽

pp. 222 ◽

Cited By ~ 1

Author(s):

Sungchul Lee ◽

Ju-Yeon Jo ◽

Yoohwan Kim

Keyword(s):

Big Data ◽

Performance Analysis ◽

Data Locality ◽

Performance Model ◽

Data System ◽

Analysis Model ◽

Physical Test ◽

Data Movement ◽

Hadoop Distributed File System ◽

Hadoop System

Background: Hadoop has become the base framework on the big data system via the simple concept that moving computation is cheaper than moving data. Hadoop increases a data locality in the Hadoop Distributed File System (HDFS) to improve the performance of the system. The network traffic among nodes in the big data system is reduced by increasing a data-local on the machine. Traditional research increased the data-local on one of the MapReduce stages to increase the Hadoop performance. However, there is currently no mathematical performance model for the data locality on the Hadoop. Methods: This study made the Hadoop performance analysis model with data locality for analyzing the entire process of MapReduce. In this paper, the data locality concept on the map stage and shuffle stage was explained. Also, this research showed how to apply the Hadoop performance analysis model to increase the performance of the Hadoop system by making the deep data locality. Results: This research proved the deep data locality for increasing performance of Hadoop via three tests, such as, a simulation base test, a cloud test and a physical test. According to the test, the authors improved the Hadoop system by over 34% by using the deep data locality. Conclusions: The deep data locality improved the Hadoop performance by reducing the data movement in HDFS.

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