Research on Database Schema Comparison of Relational Databases and Key-Value Stores

2014 ◽  
Vol 1049-1050 ◽  
pp. 1860-1863
Author(s):  
Peng Zhou ◽  
Mei Li ◽  
Jing Huang ◽  
Hua Fang
Keyword(s):  
Big Data ◽  
Relational Databases ◽  
Efficient Solution ◽  
Rapid Development ◽  
Internet Technology ◽  
Database Schema ◽  
Typical Application ◽  
Existing Problems ◽  
Management Capacity ◽  
High Scalability

With the rapid development of Internet technology, the management capacity of traditional relational databases becomes relatively inefficient when facing the access and processing of big data. As a kind of non-relational databases, the key-value stores, with its high scalability, provide an efficient solution to the problem. This article introduces the concept and features of Key-Value stores, and followed by the comparison with the traditional relational databases, and an example is illustrated to explain its typical application and finally the existing problems of Key-Value stores are summarized.

scholarly journals APPLICATION OF BIG DATA IN ENTERPRISE MANAGEMENT

2021 ◽  
Author(s):  
FENG GUO ◽  
HUI-LIN QIN
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Rapid Development ◽  
Enterprise Management ◽  
Complex Data ◽  
Real Economy ◽  
Existing Problems ◽  
Other Information ◽  
Big Data Application ◽  
Deep Integration

With the continuous development of information technology, enterprises have gradually entered the era of big data. How to analyze the complex data and find out the useful information to promote the development of enterprises is becoming more and more important in the modernization of science and technology. This paper expounds the importance and existing problems of big data application in enterprise management, and briefly analyzes and discusses its application in enterprises and its future development direction and trend. With the rapid development of Internet of things, cloud computing and other information technology, the world ushered in the era of big data. It has become a trend to promote the deep integration of Internet, big data, artificial intelligence and real economy. Due to the rapid development of economy, the amount of data information generated in the process of consumption and production is very large. Under the traditional management mode, enterprises can not meet the needs of the current social and economic development. However, the application of big data technology in enterprises can achieve better analysis and Research on these data information, so as to provide reliable data basis for enterprises to carry out various business management decisions.

Improving the performance of precision poverty alleviation based on big data mining and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Lejie Wang
Keyword(s):  
Machine Learning ◽  
Economic Growth ◽  
Big Data ◽  
Low Income ◽  
Large Scale ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Internet Technology ◽  
Rapid Economic Growth ◽  
Causes Of Poverty

Since the reform began in our country, with the rapid economic growth in recent years, the income level has grown extremely unequal, and it is difficult for the low-income poor to benefit from the rapid economic growth. The most important prerequisite for the fight against poverty is the accurate identification of the causes of poverty. To date, our country has not reached the level of maturity required to accurately study the causes of poverty in various households. However, with the rapid development of Internet technology and big data technology in recent years, the application of large-scale data technology and data extraction algorithms to poverty reduction can identify truly poor households faster and more accurately. Compared with traditional machine learning algorithms, there are no machine storage and technical constraints, can use a large amount of data and rely on multiple data samples.

scholarly journals 60 Years of Databases

2021 ◽  
pp. 040-071
Author(s):  
V.A. Reznichenko ◽  
Keyword(s):  
Big Data ◽  
Soviet Union ◽  
Research And Development ◽  
Relational Databases ◽  
Rapid Development ◽  
Conceptual Modeling ◽  
Query Languages ◽  
Active Object ◽  
The Soviet Union ◽  
Database Research

The article provides an overview of research and development of databases since their appearance in the 60s of the last century to the present time. The following stages are distinguished: the emergence formation and rapid development, the era of relational databases, extended relational databases, post-relational databases and big data. At the stage of formation, the systems IDS, IMS, Total and Adabas are described. At the stage of rapid development, issues of ANSI/X3/SPARC database architecture, CODASYL proposals, concepts and languages of conceptual modeling are highlighted. At the stage of the era of relational databases, the results of E. Codd's scientific activities, the theory of dependencies and normal forms, query languages, experimental research and development, optimization and standardization, and transaction management are revealed. The extended relational databases phase is devoted to describing temporal, spatial, deductive, active, object, distributed and statistical databases, array databases, and database machines and data warehouses. At the next stage, the problems of post-relational databases are disclosed, namely, NOSQL-, NewSQL- and ontological databases. The sixth stage is devoted to the disclosure of the causes of occurrence, characteristic properties, classification, principles of work, methods and technologies of big data. Finally, the last section provides a brief overview of database research and development in the Soviet Union.

scholarly journals A Survey on Unsupervised K-Means Algorithm in Big Data Environment

2021 ◽  
pp. 1-8
Author(s):  
Fatama Sharf Al-deen ◽  
Fadl Mutaher Ba-Alwi
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Big Data ◽  
Literature Review ◽  
Relational Databases ◽  
Rapid Development ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Prominent Feature ◽  
Data Environment

Due to the rapid development in information technology, Big Data has become one of its prominent feature that had a great impact on other technologies dealing with data such as machine learning technologies. K-mean is one of the most important machine learning algorithms. The algorithm was first developed as a clustering technology dealing with relational databases. However, the advent of Big Data has highly effected its performance. Therefore, many researchers have proposed several approaches to improve K-mean accuracy in Big Data environment. In this paper, we introduce a literature review about different technologies proposed for k-mean algorithm development in Big Data. We demonstrate a comparison between them according to several criteria, including the proposed algorithm, the database used, Big Data tools, and k-mean applications. This paper helps researchers to see the most important challenges and trends of the k-mean algorithm in the Big Data environment.

scholarly journals Countermeasure Analysis on Internet Logistics

2018 ◽  
Vol 38 ◽  
pp. 01036 ◽  
Author(s):  
Shao Ying Teng ◽  
Xiao Jun Li ◽  
Zhi Zhao ◽  
Peng Lei Qin ◽  
Ya Ya Lu
Keyword(s):  
Industrial Revolution ◽  
Rapid Development ◽  
Internet Technology ◽  
The Internet ◽  
Logistics Industry ◽  
Existing Problems ◽  
New Business ◽  
Deep Integration ◽  
Intelligent Logistics ◽  
Further Development

The rapid development of Internet technology has caused a series of industrial revolution, which has provided strong impetus for economic development. The Internet + concept puts forward the deep integration between the Internet and traditional industries, which points out the direction for the development of various industries. For the logistics industry, "Internet +" provides a new way of transformation, and intelligent logistics, smart logistics and green logistics bring new business value to the logistics industry. This paper analyzes the current situation of the logistics industry in the context of Internet +, finds out the existing problems, and proposes corresponding solutions to provide the impetus for further development of the logistics industry.

scholarly journals RESEARCH ON BEHAVIORAL MOTIVATION INFERENCE METHOD FOR GEOGRAPHICAL SPATIOTEMPORAL LARGE DATA

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 725-729
Author(s):  
J. W. Li ◽  
W. D. Chen ◽  
Y. Ma ◽  
N. Yu ◽  
X. Li ◽  
...  
Keyword(s):  
Big Data ◽  
Association Rules ◽  
User Behavior ◽  
Rapid Development ◽  
Frequent Itemsets ◽  
Internet Technology ◽  
Personalized Recommendation ◽  
User Interest ◽  
Geographic Context ◽  
Spatio Temporal

Abstract. Along with the rapid development of Internet technology, GNSS technology and mobile terminals, a large amount of information including geographical location and time attributes has been generated. Faced with large and complex Internet geospatial data, how to quickly and accurately extract valuable reference information becomes an urgent problem to be solved. And the user's demand for personalized information of recommendation information is getting stronger and stronger, and researching efficient and accurate personalized recommendation system has good application value. In this paper, based on the application requirements of personalized recommendation information, the GIS platform and related recommendation algorithms are used to fully exploit the user and location based on geographic space-time big dataIt is divided into user explicit interest and user implicit interest, and then establishes a scientific and efficient user behavior motivation prediction model based on geographic situation. User interest information can be obtained from explicit interest information, implicit interest information and geographic situation interest information. Geographical environment, geographic location and other related context information. By introducing time factors, it is used to update and improve the user real-time interest model to achieve accurate prediction of user behavior motives under geographic spatio-temporal big data. Use Apriori algorithm to calculate the support and determine the current Frequent itemsets of user interest in geographic context, using frequent itemsets to generate strong association rules, and realizing the analysis of user behavior motives based on geography context. For geographic spatio-temporal big data, this paper proposes a personalized hybrid recommendation algorithm, which is based on users. Effective combination of collaborative filtering algorithms and association rules for geographic context-user behavioral interest adaptation.

scholarly journals Application Strategy of Big Data Processing Technology in Intelligent Transportation

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Yaohua Xie ◽  
Xin Zhou
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Traffic Management ◽  
Traffic Accidents ◽  
Rapid Development ◽  
Intelligent Transportation ◽  
Processing Technology ◽  
Internet Technology ◽  
Big Data Processing ◽  
Artificial Intelligence Technology

With the rapid development of China’s society and economy, the process of urbanization has been accelerated, and the transportation system has become more complicated, especially the frequent occurrence of traffic accidents, traffic congestions, and environmental pollution. In the context of the rapid development of Internet technology, digital technology, artificial intelligence technology, etc. We apply them to traffic management as effective ways to improve China’s traffic operation management. Based on big data processing technology, this paper discusses its application strategy in intelligent transportation, in hope of serving as a reference.

scholarly journals A Refection on Digitalized Creation of Chinese Internet Big Movies in the Context of Internet

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Liang Lei ◽  
Kunhao Ni ◽  
Ying Cui
Keyword(s):  
Big Data ◽  
Rapid Development ◽  
Internet Technology ◽  
Production Model ◽  
The Internet ◽  
Artistic Creation ◽  
Chinese Film ◽  
Big Data Technologies ◽  
New Type ◽  
New Phenomena

The 21st century is the era of the Internet, where movies see new vitality in artistic creation under the concept of the Internet+. Internet Big movies, a new type of films born with the advancement of Internet technology, have seen rapid development in less than a decade, having gradually established a mature production model. This paper provides reflection and analysis on the new phenomena in Chinese film production that involve numerous applications of big data technologies.

scholarly journals ПОРІВНЯННЯ SQL І NOSQL БАЗ ДАНИХ НА ПРИКЛАДІ ПРОЕКТУВАННЯ АФФІЛЕЙТ РЕПОРТ СИСТЕМ

2020 ◽  
pp. 83-89
Author(s):  
Дмитро Тереник ◽  
Георгій Кучук Анатолійович
Keyword(s):  
Data Storage ◽  
Database Management ◽  
Relational Databases ◽  
Rapid Development ◽  
Systems Design ◽  
Large Data ◽  
Database Management System ◽  
Database Schema ◽  
Business Executives ◽  
Major Direction

Nowadays, due to the rapid development of social networks and the blogger culture, there is a tendency to use affiliate systems to promote their product. The Affiliate Reporting Service is a service offered to customers who want to analyze the affiliate systems' performance data. These systems are used by business executives and business owners to analyze ecommerce data and convert it into profit/expense data to adjust their business path further. This type of service includes data storage for all affiliates, data archive management, conversion of advertising campaigns, trend tracking, and more. These systems are based on large data sets that need to be stored correctly and safely stored and processed using database management systems. There are two major direction: SQL and NoSQL, relational and non-relational databases. The differences between them are how they are designed, what types of data they support, how they store information, how they support information security. A rigid relational database schema helps maintain the security and integrity of data when stored and modified. The lack of a rigid database schema and the need to change the entire structure of the table with a minimal change in the storage concept, make it easier to work with non-relational databases and subsequently support them, but it also has its disadvantages. It is important to understand that the tasks are different and the methods for solving them are also different; Choosing a database and database management system is a complex multi-parameter task and is one of the most important steps in developing such applications. Properly selected database will reduce the monetary and time costs associated with the development of the software, as well as facilitate system support in the future. The purpose of the article is to compare relational and non-relational databases by different metrics used in Affiliate Reporting Systems Design. In particular, a performance analysis was conducted on the performance of various operations, on the basis of which conclusions were drawn about the use of a particular database.

scholarly journals Smart City Rail Automatic Fare Collection System Under the Background of Big Data

2022 ◽  
Vol 2146 (1) ◽  
pp. 012013
Author(s):  
Shurun Xie
Keyword(s):  
Big Data ◽  
Smart City ◽  
System Development ◽  
Rapid Development ◽  
Response Speed ◽  
Fast Response ◽  
Internet Technology ◽  
Collection System ◽  
Human Material ◽  
Low Efficiency

Abstract With the rapid development of Internet technology, the process of urban construction is accelerating and the level of urbanization is further improved. In the smart city rail automatic fare collection system, there are a large number of data information and data that need to be processed manually. The traditional manual method not only consumes human, material and financial resources, but also has low efficiency. Therefore, this paper proposes a smart city rail automatic fare collection system based on big data design. Firstly, this paper expounds the concept of smart city rail transit and studies the function of automatic fare collection system. Then it studies the definition and characteristics of big data, designs the method of system development, and tests the performance of the system. The test results show that the system runs smoothly, accounts for a relatively small amount of memory, has a fast response speed and low delay. Most passengers are satisfied with the system.

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