Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

An Overview of Big Data Industry in China

The year of 2011 is considered the first year of big data market in China.Compared with the global scale,China's big data growth will be faster than the global average growth rate,and China will usher in the rapid expansion of big data market in the next few years.This paper presents the overall big data development in China in terms of market scale and development stages,enterprise development in the industry chain,the technology standards,and industrial applications.The paper points out the issues and challenges facing big data development in China and proposes to make polices and create support approaches for big data transactions and personal privacy protection.

Analysis and Visualization of Marketing, Statistical and Macroeconomic Data With GIS

The most common way to analyze economics data is to use statistics software and spreadsheets.The paper presents opportunities of modern Geographical Information System （GIS） for analysis of marketing, statistical, and macroeconomic data. It considers existing tools and models and their applications in various sectors. The advantage is that the statistical data could be combined with geographic views, maps and also additional data derived from the GIS. As a result, a programming system is developed, using GIS for analysis of marketing, statistical, macroeconomic data, and risk assessment in real time and prevention. The system has been successfully implemented as web-based software application designed for use with a variety of hardware platforms （mobile devices, laptops, and desktop computers）. The software is mainly written in the programming language Python, which offers a better structure and supports for the development of large applications. Optimization of the analysis, visualization of macroeconomic, and statistical data by region for different business research are achieved. The system is designed with Geographical Information System for settlements in their respective countries and regions. Information system integration with external software packages for statistical calculations and analysis is implemented in order to share data analyzing, processing, and forecasting. Technologies and processes for loading data from different sources and tools for data analysis are developed. The successfully developed system allows implementation of qualitative data analysis.

Coordination for a Closed-loop Supply Chain with Recycling Platform Considering Dynamic Setting

This paper presents a dynamic closed-loop supply chain(CLSC)model,incorporating a manufacturer,a retailer,and an internet recycling platform(IRP),utilizing differential game theory while considering the forgetting effect of consumers.The model encompasses factors such as the quality level of used products and Big Data marketing(BDM),comparing optimal equilibriums under decentralized and cooperative decision scenarios.To effectively coordinate the dynamic CLSC at each time point,we propose a revenue-sharing and cost-sharing(RSCS)combined contract.In addition to ensuring reasonable sharing of revenues and costs,this contract allows the manufacturer to flexibly adjust wholesale prices for final products and transfer prices for used products in order to distribute profits appropriately and achieve Pareto optimality within the CLSC system.Furthermore,our results indicate that there exists a threshold for Big Data marketing efficiency;high-efficiency BDM not only facilitates increased recycling on Internet platforms but also reduces unit recycling costs for enterprises.Interestingly,when implementing the combined contract,Big Data marketing efficiency does not impact the transfer price paid by manufacturers to Internet recycling platforms.

A case study on the shareholder network effect of stock market data:An SARMA approach

One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models have been proposed.Most of them assume that the random noise associated with each subject is independent.However,dependence might still exist within this random noise.Ignoring this valuable information might lead to biased estimations and inaccurate predictions.In this article,we study a spatial autoregressive moving average model with exogenous covariates.Spatial dependence from both response and random noise is considered simultaneously.A quasi-maximum likelihood estimator is developed,and the estimated parameters are shown to be consistent and asymptotically normal.We then conduct an extensive analysis of the proposed method by applying it to the Chinese stock market data.

Data Sharing and Innovative Utilization:A Study on Shenzhen's Multi-Actor Interaction Mechanism of Smart City Construction

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.