15 May 2022, Volume 8 Issue 3
TOPIC: DEVELOPING DATA FACTOR MARKET
Developing Data Factor Market
2022, 8(3):  1-2.  doi:10.11959/j.issn.2096-0271.2022045-1
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Features and transaction modes of data products in data markets
Lihua HUANG, Yifan DOU, Mengke GUO, Qifeng TANG, Gen LI
2022, 8(3):  3-14.  doi:10.11959/j.issn.2096-0271.2022045
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Developing the markets of data as a factor of production is the key in the efficient allocation of data factor.However, the early practices of data markets in China have revealed a series of problems, which urgently calls for a systematic review and analysis on the data market theoretical mechanisms.The circulation process of data products was analyzed from different perspectives, such as transaction cost theory, electronic market framework, and electronic trading mode.And it was further proposed that the effects of the data computability were two-fold.On the one hand, the computability enabled data to be analyzed so as to fit in the specific demand in certain industries.On the other hand, the computability was also likely to remove the data transaction process from the market, also known as platform disintermediation.Based on the classical theoretical framework of electronic market, the offerings of data products were divided into four quadrants and analysis was conducted correspondingly.Finally, suggestions for data product suppliers and data transaction platform providers were put forward.

BoxedData: a data product form based on databox
Yazhen YE, Yangyong ZHU
2022, 8(3):  15-25.  doi:10.11959/j.issn.2096-0271.2022030
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Same as those usual product markets, data products circulating in a data market can be categorized into standard products and non-standard products.Currently, standardized data products such as music, images and video clips are effectively circulating in the market, while large-scale big data product in a broad sense is facing numerous circulation obstacles.One such obstacle is the measurement and valuation of data products, which requires a measurable standard data product form to solve.On the basis of the data box model, BoxedData was proposed, as a standard form of data products.A data product that adopts the form of BoxedData consists of two parts, which are inbox data and packing materials.Inbox data refers to the cubic data structure with three dimensions of time, space and content, which generally includes images, shapes, videos, sound, text, structured data and other types of data.Packing materials includes product registration certificate, product instructions, product quality certificate, product compliance certificate and other documents.BoxedData aims to provide data factor market with a standard data product form which is measurable and evaluable.

From data quality to data products quality
Li CAI, Yangyong ZHU
2022, 8(3):  26-39.  doi:10.11959/j.issn.2096-0271.2022040
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For a long time, the purpose of data quality research is to fulfill requirements of the normal operation of the organization’s own information system.With the construction and development of data market, the requirements on data quality have changed from “self-use” to “other use” and “need for supervision”.The data products quality in the data market is the focus of data users (buyers) and market regulators.The demands of users and regulators for data product quality were analyzed, and a framework of data product quality was proposed innovatively.On this basis, taking BoxedData as an example, the corresponding quality dimensions, quality indicators and quality assessment models were construct from three aspects of time, space and content integrity.The quality framework was suitable for detecting and assessing resource data products, and could provide effective detection methods and standards for data product buyers and market regulators.

Authenticating and licensing architecture of data rights in data trade
Qifeng TANG, Zhiqing SHAO, Yazhen YE
2022, 8(3):  40-53.  doi:10.11959/j.issn.2096-0271.2022029
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Data is a key factor of production in digital economy and establishing a factor market of data is inevitable.The development of data factor market includes efforts in the fields of the authentication of data rights, object of transaction, pricing mechanics, exchange platform, trade regulation and so on.The rights and authentication process necessary for a data product or data service to be traded in a data exchange were explored systematically.The form of transaction object in data trade was designed as “data product/service + a right”.A variety of licenses for different forms of data products and data services were further designed, and a licensing system supporting the exchange of data was formed.

Research on the mechanism of data transaction based on multi-party computation
Xiaoxia LIU, Jiaxi ZHANG, Shen WANG, Zuyan YANG
2022, 8(3):  54-65.  doi:10.11959/j.issn.2096-0271.2022028
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Recently, data factor transaction and market construction have received high-level attention.Different from traditional factors of production, data factors have economic characteristics such as “quasi-public goods”, information asymmetry and negative externality, which are the primary reasons hindering the large-scale circulation of data at the current stage.By taking these economics characteristics into consideration, and from the perspective of technique, the practical application of data transaction mechanism based on multi-party computation under current circumstance of policy and market was explored, through a transaction mechanism of “data is usable but invisible, usage of data is controllable and measurable”, which provided a technical solution for large-scale circulation of data.In the meantime, the data service based on multiparty computation was analyzed, which was already launched on the data transaction platform of Beijing International Data Exchange.And the practical implementation of large-scale data circulation based on multi-party computation and data factor market construction were provided.

Comparative study on laws and regulations related to data transaction
Yingzi WEN, Yang QU, Xudong ZHANG, Jun XU, Jianping LI
2022, 8(3):  66-77.  doi:10.11959/j.issn.2096-0271.2022043
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As the country has identified data as a factor of production, local governments have accelerated legislation related to data transactions, aiming to explore and speed up the construction of data factor market and promote the development of digital economy.The relevant laws and regulations of data transactions were analyzed.It was considered that there were basically consensuses on transaction principles, prohibited transaction data, data security, and the provisions were relatively clear and had certain operability.While the regulations on the subject matter and data ownership were still not clear and difficult to be implemented.At the same time, there were still problems such as difficult data security supervision and imperfect data transaction ecology.Finally, some ideas for formulating data transaction regulations and constructing data trading market in the future were put forward.

Review of data-related international tax systems
Bofeng XIE
2022, 8(3):  78-86.  doi:10.11959/j.issn.2096-0271.2022044
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The data tax corresponding to the data element as a new factor of production does not exist at present, but there exist certain taxes related to it.The theoretical connection between digital service tax, digital asset tax and intangible asset tax and data tax was analyzed.The practice of each country in these tax systems was introduced.The close connection between related taxes and data tax was shown from the taxation scope and other taxation elements.And the corresponding review and analysis were developed.Based on the above international practices, it was believed that the existing relevant tax systems had important implications for data taxation, and the design of data taxation should emphasize both incentives and regulations, and linkage between policies and collection and management.

STUDY
A review of blockchain applications in personalized recommender systems
Xiaoying XU, Xi CHEN, Yuan CHEN, Yongjing XIE
2022, 8(3):  87-102.  doi:10.11959/j.issn.2096-0271.2022031
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Blockchain, as an emerging technology, provides a brand-new idea for the improvement of personalized recommender systems with its characteristics of decentralization, tamper-proof, anonymity and traceability.Therefore, the main problems faced by the recommender systems in recent years and the opportunities brought by blockchain technology were summarized firstly.Then, literature analysis was adopted to analyze and summarize the research on the application of blockchain technology in recommender systems from four aspects: time distribution, literature types, research questions and evaluation indicators.The results show that the blockchain is of great significance for solving the problems of data security and privacy protection, data sharing, data trustworthiness and recommendation transparency of recommender systems.Existing studies mainly focus on solving the problem of data security and privacy protection in recommender systems, while further breakthroughs are needed in cross-platform data sharing, design of incentive mechanisms and system scalability.

A semi-supervised deep learning algorithm combining consistency regularization and manifold regularization
Jie WANG, Songyan ZHANG, Jiye LIANG
2022, 8(3):  103-114.  doi:10.11959/j.issn.2096-0271.2022027
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Semi-supervised learning has been widely used in big data analysis.Currently, one of the hot research topics in semisupervised deep learning is consistency-based methods.However, such methods do not take into account the manifold structure of the data, which may cause a portion of similar samples to get very different outputs, resulting in degraded classifier performance.To address this problem, a semi-supervised deep learning algorithm that combines consistency regularization with manifold regularization was proposed.The algorithm imposed a consistency constraint on the model while constructing a graph and adding a smoothing loss to achieve smoothing within the local neighborhood of each sample point and between adjacent (connected) sample points, thus improving the generalization performance of the semisupervised learning algorithm.The results on several image and text datasets show that the proposed algorithm is more effective compared with other semi-supervised deep learning algorithms.

A Chinese text sentiment analysis method combining language knowledge and deep learning
Kangting XU, Wei Song
2022, 8(3):  115-127.  doi:10.11959/j.issn.2096-0271.2022026
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At present, in the research of Chinese text emotion analysis, the method based on semantic rules and emotion dictionary usually needs to set the emotional threshold manually.However, the method based on deep learning can’t fully extract emotional features because it fails to use language knowledge such as semantic rules and emotional dictionary.As to shortcomings of two methods, a text emotion analysis method combining language knowledge and deep learning was proposed.Firstly, the key emotional segments in the text were extracted according to the semantic rules.Secondly, more explicit emotion words were extracted from the key emotional segments according to the emotional dictionary to construct the emotion set.Thirdly, the deep level features were extracted from the original text, key emotional segments and emotional set by using the deep learning model.Finally, the features were weighted and fused, and the classifier was used to judge the emotional polarity.The experimental results show that compared with the deep learning model without language knowledge, this method has significantly improved the ability of emotional polarity classification.

A fast text structuring methodology of TCM medical records based on NLP
Xiaoxia XIAO, Mingting LIU, Fengtianci YANG, Jianjianxian LIU, Yang YANG, Yue SHI
2022, 8(3):  128-139.  doi:10.11959/j.issn.2096-0271.2022025
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Traditional Chinese medicine (TCM) medical records are the most valuable documents for TCM doctors to learn clinical experience.The structured TCM medical records are conducive to extract the clinic knowledge based on machine learning and other methods, which can accelerate the inheritance of TCM.A fast text structuring methodology of TCM medical records based on natural language processing(NLP)was proposed to structure the clinic cases.Essence of Chinese Modern Famous Chinese Medical Records was selected as the medical record structuring objects,and the text in the screenshots of the medical records was recognized by optical character recognition (OCR) and the text was initially structured.A simple symptom dictionary was constructed, and the improved N-gram model combined with the dictionary was used to recognize the symptoms, signs and other words in the text, and the dictionary was updated in the structuring process.At last, 4 754 text medical records were structured.The final model was test on 666 medical records selected randomly from the corpus, and its F1 value reached 82.99%.

FORUM
Digital economics in metaverse: state-of-the-art, characteristics, and vision
Chenhuizi WANG, Wei CAI
2022, 8(3):  140-150.  doi:10.11959/j.issn.2096-0271.2022048
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Metaverse has become a very popular technology buzzword at the end of 2021, since Facebook changed its name to Meta, indicating their long-term commitment tometaverse.Firstly, the technical development process to expound on the inevitability and necessity of metaverse was reviewd.Afterward, the risks and challenges of the decentralized digital economy were revealed, through the analysis of the overseas metaverse digital economy.Lastly, it was pointed out that the key spiritual core of decentralization lies in the global anti-monopoly ideology, and the future of the domestic metaverse industry was envisioned.

Research on calculation method of China’s big data industry output value
Mei YANG, Wei LI, Siyuan QIAO, Wei LIU
2022, 8(3):  151-160.  doi:10.11959/j.issn.2096-0271.2022024
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At present, problems of big data industry, such as unclear industrial boundaries, unclear enterprise definitionand inconsistent production value measurement methods, still exist.At this stage, under the objective reality of China’s big data industry development, the big data industry chain was divided into five aspects: data resource layer, basic hardware layer, general software layer, industry application layer and security ensure layer.A clear definitionof big data enterprise was given.By obtaining the market revenue proportion of backbone enterprises in each segment of the relevant level and the big data revenue proportion of backbone enterprises in the segment of the main business income, the output value of big data in the segment was converted, by using the summation method.The output value of big data in China from 2016 to 2021 was estimated to be 4.4908 trillion yuan, and the annual compound growth rate of China’s big data industry will be about 25% during “the 14th Five-Year Plan” period.That is to say, the revenue of China’s big data industry is expected to exceed 3 trillion yuan by 2025.

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