Construction of Intelligent Information Platform for Preschool Education Based on Big Data

2021 ◽  
pp. 147-152
Author(s):  
Xia Yu
Keyword(s):  
Big Data ◽  
Preschool Education ◽  
Information Platform ◽  
Intelligent Information

Predictive Analytics

2015 ◽  
pp. 12-29 ◽  
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim
Keyword(s):  
Logistic Regression ◽  
Big Data ◽  
Multilevel Modeling ◽  
Polynomial Regression ◽  
Predictive Analytics ◽  
Intelligent Information ◽  
Complex Relationships ◽  
Analytical Tools ◽  
Existing Data ◽  
Organizational Performances

Predictive analytics and modeling are analytical tools for knowledge discovery through examining and capturing the complex relationships and patterns among the variables in the existing data in efforts to predict the future organizational performances. Their uses become more common place due largely to collecting massive amount of data, which is referred to as “big data,” and the increased need to transform large amounts of data into intelligent information (knowledge) such as trends, patterns, and relationships. The intelligent information can then be used to make smart and informed data-based decisions and predictions using various methods of predictive analytics. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools of predictive analytics. The chapter provides a detailed coverage of some of the predictive analytics tools such as Simple and Multiple-Regression, Polynomial Regression, Logistic Regression, Discriminant Analysis, and Multilevel Modeling.

Predictive Analytics

2017 ◽  
pp. 49-66
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim
Keyword(s):  
Logistic Regression ◽  
Big Data ◽  
Multilevel Modeling ◽  
Polynomial Regression ◽  
Predictive Analytics ◽  
Intelligent Information ◽  
Complex Relationships ◽  
Analytical Tools ◽  
Existing Data ◽  
Organizational Performances

Predictive analytics and modeling are analytical tools for knowledge discovery through examining and capturing the complex relationships and patterns among the variables in the existing data in efforts to predict the future organizational performances. Their uses become more common place due largely to collecting massive amount of data, which is referred to as “big data,” and the increased need to transform large amounts of data into intelligent information (knowledge) such as trends, patterns, and relationships. The intelligent information can then be used to make smart and informed data-based decisions and predictions using various methods of predictive analytics. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools of predictive analytics. The chapter provides a detailed coverage of some of the predictive analytics tools such as Simple and Multiple-Regression, Polynomial Regression, Logistic Regression, Discriminant Analysis, and Multilevel Modeling.

scholarly journals Imbalance and breakout in the post-epidemic era: Research into the spatial patterns of freight demand network in six provinces of central China

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250375
Author(s):  
Yin Huang ◽  
Runda Liu ◽  
Shumin Huang ◽  
Gege Yang ◽  
Xiaofan Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Spatial Patterns ◽  
Southern China ◽  
Northern China ◽  
Central China ◽  
Chain Reaction ◽  
Information Platform ◽  
Tail Distribution ◽  
Demand Information ◽  
A Chain

This study aims to explore the freight demand network spatial patterns in six provinces of central China from the perspective of the spread of the epidemic and the freight imbalance and breakout. To achieve this purpose, the big data of “cart search” demand information provided by small and medium freight enterprises on the freight information platform are analyzed. 343,690 pieces of freight demand big data on the freight information platform and Python, ArcGIS, UCINET, and Gephi software are used. The results show that: (1) The choke-point of unbalanced freight demand network is Wuhan, and the secondary choke-points are Hefei and Zhengzhou. (2) In southern China, a chain reaction circle of freight imbalance is formed with Wuhan, Hefei, and Nanchang as the centers. In northern China, a chain reaction circle of freight imbalance is formed with Zhengzhou and Taiyuan as the centers. (3) The freight demand of the six provinces in central China exhibits typical characteristics of long tail distribution with large span and unbalanced distribution. (4) The import and export of freight in different cities vary greatly, and the distribution is unbalanced. This study indicates the imbalance difference, chain reaction, keys and hidden troubles posed by the freight demand network. From the perspectives of freight transfer breakout, freight balance breakout, freight strength breakout, and breakout of freight periphery cities, we propose solutions to breakouts in the freight market in six provinces of central China in the post-epidemic era.

scholarly journals ARCHITECTURE OF THE INTELLECTUAL INFORMATION SYSTEM TO SUPPORT EXPERT DECISIONS ON STRATEGIC INNOVATIVE ENERGY DEVELOPMENT

2020 ◽  
Author(s):  
А.Н. Копайгородский ◽  
Т.Г. Мамедов
Keyword(s):  
Information System ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Energy Sector ◽  
Energy Development ◽  
Innovative Development ◽  
Intelligent Information ◽  
Main Component ◽  
Data Analysis Methods

В статье рассмотрены методы построения интеллектуальной информационной системы для поддержки экспертных решений по стратегическому инновационному развитию энергетики. Обоснована необходимость применения методов анализа Больших данных (Big Data). Представлена архитектура интегрированного хранилища интеллектуальной информационной системы, основным компонентом которой является система онтологий, объединяющая данные и знания из различных источников. The article discusses methods of building an intelligent information system to support expert decisions on strategic innovative development of the energy sector. The necessity of using Big Data analysis methods has been substantiated. Architecture of an integrated repository of an intelligent information system is presented, in which the main component is a system of ontologies on the basis of which information, data and knowledge from various sources are combined.

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