APPLICATION OF "BIG DATA" IN OPERATIONAL AND INTELLECTUAL INSPECTION OF SOCIO-ECONOMIC INFORMATION

2020 ◽  
Vol 7 (04) ◽  
Keyword(s):  
Big Data ◽  
Economic Information

scholarly journals Economic Information Analysis and Its Application in Decision-Making under the Background of Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Simin Chen ◽  
Ieong Sanweng
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Industrial Development ◽  
Information Analysis ◽  
Running Speed ◽  
Economic Information ◽  
Internet Age ◽  
Economic Decision Making ◽  
Service Role ◽  
Industrial Economic

In the Internet age, data information is expanding at an alarming rate, and at the same time, it is showing a trend of diversification and fragmentation. How to make reasonable use of this intricate information and effectively use it in the work to give full play to the value of big data (BD) seems urgent. Based on this, this paper studies the economic information analysis and decision-making system under the background of BD. This article first analyzes the development status of our country’s industrial economic decision-making technology, then studies the service role of BD on macroeconomic decision-making in industrial development, then conducts an in-depth study of industrial economic information analysis methods and trends, and designs an industrial economic information model. Finally, this article tested the running speed and interactive function of the system. The test results show that the running speed of the system is very fast, and the interactive function is very accurate.

scholarly journals Design of Enterprise Economic Information Management System Based on Big Data Integration Algorithm

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Xiao Liu
Keyword(s):  
Big Data ◽  
Data Management ◽  
Information Management ◽  
Data Storage ◽  
Management System ◽  
Information Management System ◽  
Economic Benefits ◽  
Current Status ◽  
Integration Algorithm ◽  
Economic Information

In economic growth, the gradual increase in the effect of information technology makes the enterprise economic information management increasingly important for the survival and development of the enterprises. This paper designs an enterprise economic information management system for the complex internal economic information management business and process of enterprises. It provides daily office, information access, document preview, and transmission. The proposed design (i) copes with the inconsistency and irregularity of enterprise economic information data, (ii) quickly obtains valuable information from these massive high-frequency data, and (iii) improves the economic benefits of data assets and data management efficiency. The printing function systematizes the information management for departments such as enterprise economic information, personnel, and production. The main focus of this research includes the mode, framework, and function of the whole system software. Moreover, it also comprises of the use of Internet platform big data technology to realize the practicality, stability, and security of the system database algorithm, which has been practically used by enterprises to improve office efficiency and meet the needs of daily management of enterprises. Based on the analysis of the current status of enterprise big data application, this paper constructs an enterprise economic informational management system based on big data and also describes in detail the key technologies of enterprise economic informational data management from three aspects: NoSQL-based big data storage management, Hadoop-based economic informational big data informational and economic informational big data analysis, and mining algorithm. Provide theoretical basis and basic technical support for online decision analysis.

scholarly journals Discretization Algorithm for Incomplete Economic Information in Rough Set Based on Big Data

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1245
Author(s):  
Xiangyang Li ◽  
Yangyang Shen
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Rough Set ◽  
Rough Sets ◽  
Application Performance ◽  
Economic Information ◽  
Related Information ◽  
Discretization Algorithm ◽  
Continuous Attribute ◽  
Relationship Of

Discretization based on rough sets is used to divide the space formed by continuous attribute values with as few breakpoint sets as possible, while maintaining the original indistinguishable relationship of the decision system, so as to accurately classify and identify related information. In this study, a discretization algorithm for incomplete economic information in rough set based on big data is proposed. First, the algorithm for filling-in incomplete economic information based on deep learning is used to supplement the incomplete economic information. Then, based on breakpoint discrimination, the algorithm for discretization in the rough set is used to implement the discretization based on rough set for supplementary economic information. The performance of this algorithm was tested using multiple sets of data and compared with other algorithms. Experimental results show that this algorithm is effective for discretization based on a rough set of incomplete economic information. When the number of incomplete economic information rough candidate breakpoints increases, it still has a higher computational efficiency and can effectively improve the integrity of incomplete economic information, and finally the application performance is superior.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Relation Between Level of Prefrontal Activity and Subject's Performance

1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Practice Phase ◽  
Trained Subjects ◽  
Anagram Task ◽  
Economic Information ◽  
Long Duration ◽  
Level Of Activity ◽  
The Brain ◽  
Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

“Big Data in Psychology: Methods and Applications”

2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  
Big Data

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org

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