The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at Data Exchanges.To deliver a complete p...The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at Data Exchanges.To deliver a complete picture of data flaw and transaction,this paper presents a systematic overview of the flow and transaction of personal,corporate and public data on the basis of data factor classification from various perspectives.By utilizing various sources of information,this paper estimates the volume of data generation&storage and the volume&trend of data market transactions for major economies in the world with the following findings:(i)Data classification is diverse due to a broad variety of applying scenarios,and data transaction and profit distribution are complex due to heterogenous entities,ownerships,information density and other attributes of different data types.(ii)Global data transaction has presented with the characteristics of productization,servitization and platform-based mode.(iii)For major economies,there is a commonly observed disequilibrium between data generation scale and storage scale,which is particularly striking for China.(i^v)The global data market is in a nascent stage of rapid development with a transaction volume of about 100 billion US dollars,and China s data market is even more underdeveloped and only accounts for some 10%of the world total.All sectors of the society should be flly aware of the diversity and complexity of data factor classification and data transactions,as well as the arduous and long-term nature of developing and improving relevant institutional systems.Adapting to such features,efforts should be made to improve data classification,enhance computing infrastructure development,foster professional data transaction and development institutions,and perfect the data governance system.展开更多
Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results conta...Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.展开更多
Data is the primary factor of production in the digital economy,playing a role in promoting the deep integration of the digital economy and the real economy and smoothing the national economic cycle.After entering the...Data is the primary factor of production in the digital economy,playing a role in promoting the deep integration of the digital economy and the real economy and smoothing the national economic cycle.After entering the circulation system of the real economy,data is rapidly integrated into production,circulation,consumption,distribution,and other links.It optimizes resource allocation,unblocks circulation channels,promotes accurate matching of supply and demand,stimulates emerging demand,and forms a virtuous circle of digital technology application,traditional physical enterprise transformation,and technological innovation.Integrated development is an important feature of the digital economy.Data promotes the integration of factors of production,products,enterprises,industries,and markets,which fosters a circular system with deep integration of the digital economy and the real economy.To promote the deep integration of the digital economy and the real economy,the government and business entities should take measures to improve the circular efficiency of the digital economy and the real economy.These measures include attaching importance to the role of data-driven development,improving data capacity,data development,and utilization in enterprises,exploring diverse circulation models of enterprise data,and creating typical application scenarios and industrial data spaces.展开更多
Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical ...Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.展开更多
The industrial ecosystem covers the advanced technologies,key industries and core factors in the process of developing new quality productive forces,serving as an important perspective for investigating the developmen...The industrial ecosystem covers the advanced technologies,key industries and core factors in the process of developing new quality productive forces,serving as an important perspective for investigating the development of new quality productive forces.Based on the analytical framework comprising technologies,industries and factors,this study examines the internal logic of the development of new quality productive forces.Firstly,the four industrial revolutions in history demonstrate that the industrial ecosystem has promoted a series of revolutionary technology innovations.Secondly,the industrial ecosystem promotes the formation and development of emerging industries through resource sharing,knowledge spillover,and technology diffusion.Thirdly,the industrial ecosystem achieves an innovative allocation of factors of production through the dynamic feedback loop mechanisms of data-algorithm-traffic and data-network-activity.The industrial ecosystem consists of three subsystems,including the innovation ecosystem,the business ecosystem,and the platform ecosystem.The three subsystems transcend the boundaries of innovation for new quality productive forces,broaden their application scenarios,and enhance the efficiency of factor allocation,respectively.To further advance the development of new quality productive forces,it is necessary to establish a system ensuring the coordinated development of all factors.Additionally,efforts are required to integrate the dual driving forces of technology innovation and institutional innovation,and enhance the integration of four chains,including the innovation chain,the industrial chain,the capital chain,and the talent chain.展开更多
Data is not only a key production factor but also an important foundation and strategic resource that drives economic growth and social progress in the era of digital economy. Data sharing and innovative utilization i...Data is not only a key production factor but also an important foundation and strategic resource that drives economic growth and social progress in the era of digital economy. Data sharing and innovative utilization in an ethical and responsible manner is a focus of the current studies on smart city construction. Taking Shenzhen as an example, this paper analyzes the three typical cases of data legislation, data sharing and utilization,and data-based anti-epidemic action in its smart city construction and explores the respective role of the four actors of the government, enterprises,research institutes, and the public in innovating data utilization to serve the public interests through data sharing. By studying Shenzhen’s multi-actor interaction mechanism of smart city construction, the paper tries to provide a useful experience for the construction of smart cities in China from the perspectives of data management, data sharing, and innovative data utilization.展开更多
As a new form of social wealth,digital wealth is becoming an important force in the promotion of common prosperity.In the digital economy era,digital labor and data factors are important sources for creating digital w...As a new form of social wealth,digital wealth is becoming an important force in the promotion of common prosperity.In the digital economy era,digital labor and data factors are important sources for creating digital wealth.The creation of digital commodity wealth is reflected in the process of value formation and value appreciation of digital goods,as well as in the expansion and reproduction process of data production factors.The creation of digital non-commodity wealth needs to rely on the coordination of multiple supply systems including government,enterprises,society and platforms.In the distribution of digital wealth in a socialist market economy,it is necessary to adhere to the basic distribution system with remuneration based on work as the main form and the coexistence of multiple distribution methods.It should fully reflect the value contribution of data factors while also addressing and resolving potential polarization effects caused by the distribution of digital wealth.On the basis of the new development stage,China should steadily advance the creation and accumulation of digital wealth on the basis of constantly strengthening,improving and expanding the digital economy,and should pay attention to standardizing the distribution of digital wealth in order to achieve a solid advance in the common prosperity of all the people.展开更多
Grasping the development trends and patterns of the digital economy and promoting the integrated development of the digital and the real economy is a strategic choice for China's construction of new competitive ad...Grasping the development trends and patterns of the digital economy and promoting the integrated development of the digital and the real economy is a strategic choice for China's construction of new competitive advantages.Based on case studies of the growth of e-commerce,this paper develops five microeconomic propositions about the following issues:the microeconomic features of data factor that differ from traditional production factors;optimal decision-making for digital enterprises;“data+platform”architecture,data transaction governance,and digital infrastructure supply.We apply these theoretical propositions to enterprise innovative practices in different application scenarios such as driverless vehicles and manufacturing digitalization.This paper provides a systematic and consistent theoretical framework for analyzing platform business model innovation and accurately identifying issues of institutional construction that promote the integrated development of the digital and the real economy.展开更多
文摘The public has shown great interest in the data factor and data transactions,but the current attention is overly focused on personal behavioral data and transactions happening at Data Exchanges.To deliver a complete picture of data flaw and transaction,this paper presents a systematic overview of the flow and transaction of personal,corporate and public data on the basis of data factor classification from various perspectives.By utilizing various sources of information,this paper estimates the volume of data generation&storage and the volume&trend of data market transactions for major economies in the world with the following findings:(i)Data classification is diverse due to a broad variety of applying scenarios,and data transaction and profit distribution are complex due to heterogenous entities,ownerships,information density and other attributes of different data types.(ii)Global data transaction has presented with the characteristics of productization,servitization and platform-based mode.(iii)For major economies,there is a commonly observed disequilibrium between data generation scale and storage scale,which is particularly striking for China.(i^v)The global data market is in a nascent stage of rapid development with a transaction volume of about 100 billion US dollars,and China s data market is even more underdeveloped and only accounts for some 10%of the world total.All sectors of the society should be flly aware of the diversity and complexity of data factor classification and data transactions,as well as the arduous and long-term nature of developing and improving relevant institutional systems.Adapting to such features,efforts should be made to improve data classification,enhance computing infrastructure development,foster professional data transaction and development institutions,and perfect the data governance system.
基金Under the auspices of Special Fund of Ministry of Land and Resources of China in Public Interest(No.201511001)
文摘Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.
基金the Research on Collaborative and Mutual Promotion Mechanism for Innovation and Governance of High-Quality Development of the Digital Economy,a major program approved by the National Social Science Fund of China(No.22&ZD070)the Impact of Data Factor Value Realization on Enterprises'Digital Transformation:Mechanisms,Models,and Strategies,a program funded by the National Natural Science Foundation of China(No.72373056).
文摘Data is the primary factor of production in the digital economy,playing a role in promoting the deep integration of the digital economy and the real economy and smoothing the national economic cycle.After entering the circulation system of the real economy,data is rapidly integrated into production,circulation,consumption,distribution,and other links.It optimizes resource allocation,unblocks circulation channels,promotes accurate matching of supply and demand,stimulates emerging demand,and forms a virtuous circle of digital technology application,traditional physical enterprise transformation,and technological innovation.Integrated development is an important feature of the digital economy.Data promotes the integration of factors of production,products,enterprises,industries,and markets,which fosters a circular system with deep integration of the digital economy and the real economy.To promote the deep integration of the digital economy and the real economy,the government and business entities should take measures to improve the circular efficiency of the digital economy and the real economy.These measures include attaching importance to the role of data-driven development,improving data capacity,data development,and utilization in enterprises,exploring diverse circulation models of enterprise data,and creating typical application scenarios and industrial data spaces.
基金supported by the National Basic Research Program of China (973 Program: 2013CB329004)
文摘Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.
基金supported by the major project of the National Social Science Fund of China titled“Theoretical and Practical Research on Digital Industry Cluster Innovation Based on a Deep Integration of Digital and Real Economies(No.24&ZD074)the Chinese Academy of Social Sciences'Innovation Program titled“Research on the Basic Theory and Economic Contribution of Data Factors(No.2024CJY0103).
文摘The industrial ecosystem covers the advanced technologies,key industries and core factors in the process of developing new quality productive forces,serving as an important perspective for investigating the development of new quality productive forces.Based on the analytical framework comprising technologies,industries and factors,this study examines the internal logic of the development of new quality productive forces.Firstly,the four industrial revolutions in history demonstrate that the industrial ecosystem has promoted a series of revolutionary technology innovations.Secondly,the industrial ecosystem promotes the formation and development of emerging industries through resource sharing,knowledge spillover,and technology diffusion.Thirdly,the industrial ecosystem achieves an innovative allocation of factors of production through the dynamic feedback loop mechanisms of data-algorithm-traffic and data-network-activity.The industrial ecosystem consists of three subsystems,including the innovation ecosystem,the business ecosystem,and the platform ecosystem.The three subsystems transcend the boundaries of innovation for new quality productive forces,broaden their application scenarios,and enhance the efficiency of factor allocation,respectively.To further advance the development of new quality productive forces,it is necessary to establish a system ensuring the coordinated development of all factors.Additionally,efforts are required to integrate the dual driving forces of technology innovation and institutional innovation,and enhance the integration of four chains,including the innovation chain,the industrial chain,the capital chain,and the talent chain.
基金supported by the National Natural Science Foundation of China(Project No.52078197)。
文摘Data is not only a key production factor but also an important foundation and strategic resource that drives economic growth and social progress in the era of digital economy. Data sharing and innovative utilization in an ethical and responsible manner is a focus of the current studies on smart city construction. Taking Shenzhen as an example, this paper analyzes the three typical cases of data legislation, data sharing and utilization,and data-based anti-epidemic action in its smart city construction and explores the respective role of the four actors of the government, enterprises,research institutes, and the public in innovating data utilization to serve the public interests through data sharing. By studying Shenzhen’s multi-actor interaction mechanism of smart city construction, the paper tries to provide a useful experience for the construction of smart cities in China from the perspectives of data management, data sharing, and innovative data utilization.
文摘As a new form of social wealth,digital wealth is becoming an important force in the promotion of common prosperity.In the digital economy era,digital labor and data factors are important sources for creating digital wealth.The creation of digital commodity wealth is reflected in the process of value formation and value appreciation of digital goods,as well as in the expansion and reproduction process of data production factors.The creation of digital non-commodity wealth needs to rely on the coordination of multiple supply systems including government,enterprises,society and platforms.In the distribution of digital wealth in a socialist market economy,it is necessary to adhere to the basic distribution system with remuneration based on work as the main form and the coexistence of multiple distribution methods.It should fully reflect the value contribution of data factors while also addressing and resolving potential polarization effects caused by the distribution of digital wealth.On the basis of the new development stage,China should steadily advance the creation and accumulation of digital wealth on the basis of constantly strengthening,improving and expanding the digital economy,and should pay attention to standardizing the distribution of digital wealth in order to achieve a solid advance in the common prosperity of all the people.
文摘Grasping the development trends and patterns of the digital economy and promoting the integrated development of the digital and the real economy is a strategic choice for China's construction of new competitive advantages.Based on case studies of the growth of e-commerce,this paper develops five microeconomic propositions about the following issues:the microeconomic features of data factor that differ from traditional production factors;optimal decision-making for digital enterprises;“data+platform”architecture,data transaction governance,and digital infrastructure supply.We apply these theoretical propositions to enterprise innovative practices in different application scenarios such as driverless vehicles and manufacturing digitalization.This paper provides a systematic and consistent theoretical framework for analyzing platform business model innovation and accurately identifying issues of institutional construction that promote the integrated development of the digital and the real economy.
