Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambig...Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambiguous data rights,confusing pricing,and challenges in matching.Additionally,centralized IoT data trading platforms pose risks such as privacy leakage.To address these issues,we propose a profit-driven distributed trading mechanism for IoT data.First,a blockchain-based trading architecture for IoT data,leveraging the transparent and tamper-proof features of blockchain technology,is proposed to establish trust between data owners and data requesters.Second,an IoT data registration method that encompasses both rights confirmation and pricing is designed.The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data.For pricing,we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network.Finally,an IoT data matching method is designed based on the Stackelberg game.This establishes a Stackelberg game model involving multiple data owners and requesters,employing a hierarchical optimization method to determine the optimal purchase strategy.The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated.Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.展开更多
This paper explores the data theory of value along the line of reasoning epochal characteristics of data-theoretical innovation-paradigmatic transformation and,through a comparison of hard and soft factors and observa...This paper explores the data theory of value along the line of reasoning epochal characteristics of data-theoretical innovation-paradigmatic transformation and,through a comparison of hard and soft factors and observation of data peculiar features,it draws the conclusion that data have the epochal characteristics of non-competitiveness and non-exclusivity,decreasing marginal cost and increasing marginal return,non-physical and intangible form,and non-finiteness and non-scarcity.It is the epochal characteristics of data that undermine the traditional theory of value and innovate the“production-exchange”theory,including data value generation,data value realization,data value rights determination and data value pricing.From the perspective of data value generation,the levels of data quality,processing,use and connectivity,data application scenarios and data openness will influence data value.From the perspective of data value realization,data,as independent factors of production,show value creation effect,create a value multiplier effect by empowering other factors of production,and substitute other factors of production to create a zero-price effect.From the perspective of data value rights determination,based on the theory of property,the tragedy of the private outweighs the comedy of the private with respect to data,and based on the theory of sharing economy,the comedy of the commons outweighs the tragedy of the commons with respect to data.From the perspective of data pricing,standardized data products can be priced according to the physical product attributes,and non-standardized data products can be priced according to the virtual product attributes.Based on the epochal characteristics of data and theoretical innovation,the“production-exchange”paradigm has undergone a transformation from“using tangible factors to produce tangible products and exchanging tangible products for tangible products”to“using intangible factors to produce tangible products and exchanging intangible products for tangible products”and ultimately to“using intangible factors to produce intangible products and exchanging intangible products for intangible products”.展开更多
Data pricing is a key link to promote the efficient circulation of data in the market.However,the existing methods are still insufficient in terms of pertinence,dynamism and comprehensiveness.Therefore,we proposed a d...Data pricing is a key link to promote the efficient circulation of data in the market.However,the existing methods are still insufficient in terms of pertinence,dynamism and comprehensiveness.Therefore,we proposed a data pricing prediction model based on sparrow search optimization XGBoost,aiming to provide a reference for pricing decisions in data market.First,we crawled the data transaction information of Youedata.com and performed preprocessing operations such as outlier processing,one hot encoding and logarithmic transformation on the dataset;Secondly,we conducted exploratory data analysis to understand the distribution of data and their correlation.Then,we used the LASSO algorithm to select features for the dataset and constructed a data pricing prediction model based on SSA-XGBoost.Finally,we compared and analyzed it with six machine learning models including LightGBM,GBDT,MLP,KNN,LR and XGBoost.The experimental results show that in terms of the R-squared,the prediction results of the proposed SSA-XGBoost model exceed the above six models by 4.9%,7.4%,7.1%,23.8%,12.8%,and 2.3%respectively,and are superior to the state-of-the-art work.Furthermore,the evaluation results of the five indicators of MSE,RMSE,MAE,MAPE,and RMSPE are better than other models,showing higher stability.展开更多
The real estate market is at risk to produce economic bubbles, which produce heavy financial losses while bursting. To flatten these bubbles, Germany tried since the 1960s a way of thorough analysis and high transpare...The real estate market is at risk to produce economic bubbles, which produce heavy financial losses while bursting. To flatten these bubbles, Germany tried since the 1960s a way of thorough analysis and high transparency of the real estate market as a public task. The experts from different professions in the local market effectively are included in a honorary form. The competition between the experts and also between the experts and the public institution is intentionally maintained.展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFF0610003)the BUPT Excellent Ph.D.Students Foundation(No.CX2022218)the Fund of Central University Basic Research Projects(No.2023ZCTH11).
文摘Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambiguous data rights,confusing pricing,and challenges in matching.Additionally,centralized IoT data trading platforms pose risks such as privacy leakage.To address these issues,we propose a profit-driven distributed trading mechanism for IoT data.First,a blockchain-based trading architecture for IoT data,leveraging the transparent and tamper-proof features of blockchain technology,is proposed to establish trust between data owners and data requesters.Second,an IoT data registration method that encompasses both rights confirmation and pricing is designed.The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data.For pricing,we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network.Finally,an IoT data matching method is designed based on the Stackelberg game.This establishes a Stackelberg game model involving multiple data owners and requesters,employing a hierarchical optimization method to determine the optimal purchase strategy.The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated.Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.
基金funded by“Management Model Innovation of Chinese Enterprises”Research Project,Institute of Industrial Economics,CASS(Grant No.2019-gjs-06)Project under the Graduate Student Scientific and Research Innovation Support Program,University of Chinese Academy of Social Sciences(Graduate School)(Grant No.2022-KY-118).
文摘This paper explores the data theory of value along the line of reasoning epochal characteristics of data-theoretical innovation-paradigmatic transformation and,through a comparison of hard and soft factors and observation of data peculiar features,it draws the conclusion that data have the epochal characteristics of non-competitiveness and non-exclusivity,decreasing marginal cost and increasing marginal return,non-physical and intangible form,and non-finiteness and non-scarcity.It is the epochal characteristics of data that undermine the traditional theory of value and innovate the“production-exchange”theory,including data value generation,data value realization,data value rights determination and data value pricing.From the perspective of data value generation,the levels of data quality,processing,use and connectivity,data application scenarios and data openness will influence data value.From the perspective of data value realization,data,as independent factors of production,show value creation effect,create a value multiplier effect by empowering other factors of production,and substitute other factors of production to create a zero-price effect.From the perspective of data value rights determination,based on the theory of property,the tragedy of the private outweighs the comedy of the private with respect to data,and based on the theory of sharing economy,the comedy of the commons outweighs the tragedy of the commons with respect to data.From the perspective of data pricing,standardized data products can be priced according to the physical product attributes,and non-standardized data products can be priced according to the virtual product attributes.Based on the epochal characteristics of data and theoretical innovation,the“production-exchange”paradigm has undergone a transformation from“using tangible factors to produce tangible products and exchanging tangible products for tangible products”to“using intangible factors to produce tangible products and exchanging intangible products for tangible products”and ultimately to“using intangible factors to produce intangible products and exchanging intangible products for intangible products”.
基金Supported by the National Social Science Fund of China(23BJY205)the MOE(Ministry of Education in China)Project of Humanities and Social Sciences(21YJCZH197,22YJAZH080)Shanxi Provincial Research Foundation for Basic Research(202303021221184)。
文摘Data pricing is a key link to promote the efficient circulation of data in the market.However,the existing methods are still insufficient in terms of pertinence,dynamism and comprehensiveness.Therefore,we proposed a data pricing prediction model based on sparrow search optimization XGBoost,aiming to provide a reference for pricing decisions in data market.First,we crawled the data transaction information of Youedata.com and performed preprocessing operations such as outlier processing,one hot encoding and logarithmic transformation on the dataset;Secondly,we conducted exploratory data analysis to understand the distribution of data and their correlation.Then,we used the LASSO algorithm to select features for the dataset and constructed a data pricing prediction model based on SSA-XGBoost.Finally,we compared and analyzed it with six machine learning models including LightGBM,GBDT,MLP,KNN,LR and XGBoost.The experimental results show that in terms of the R-squared,the prediction results of the proposed SSA-XGBoost model exceed the above six models by 4.9%,7.4%,7.1%,23.8%,12.8%,and 2.3%respectively,and are superior to the state-of-the-art work.Furthermore,the evaluation results of the five indicators of MSE,RMSE,MAE,MAPE,and RMSPE are better than other models,showing higher stability.
文摘The real estate market is at risk to produce economic bubbles, which produce heavy financial losses while bursting. To flatten these bubbles, Germany tried since the 1960s a way of thorough analysis and high transparency of the real estate market as a public task. The experts from different professions in the local market effectively are included in a honorary form. The competition between the experts and also between the experts and the public institution is intentionally maintained.
