Big Data Research

Issues faced by the determination of data ownership and solutions

Bo HE

2021, 7(4): 3-13. doi:10.11959/issn.2096-0271.2021034

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As data becomes a key production factor, the determination of data ownership is becoming an important issue.Firstly, the problems that need to be solved urgently from perspectives of the government, enterprise, and individual brought by the unclear determination of data ownership were analyzed, including national data sovereignty and digital governance challenges, enterprise’s data concentration and disorderly competition problems, as well as personal data protection issues.Then, the theoretical and practical dilemmas in the determination of data ownership were pointed out.Finally, on the basis of adhering to the principles of equal emphasis on development and regulation, strictly abiding by the personal information protection bottom line and classification, the solution to crack the data ownership dilemma was proposed.That is by improving the design of the legal system to establish a basic data management system and explore the rules of data ownership determination by classification, strengthening the administrative supervision measures to improve data processing transparency and personal information protection, and making the full use of technical means.

Assessment and pricing of data assets:research review and prospect

Chuanru YIN, Tao JIN, Peng ZHANG, Jianmin WANG, Jiayi CHEN

2021, 7(4): 14-27. doi:10.11959/issn.2096-0271.2021035

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In the digital economy era, data has become a new key production factor.As a new form of assets, how to manage the value of data assets has become a new research topic.Through literature research, the research results of domestic and foreign scholars on data asset value management were analyzed systematically.And the concept of data asset value index on this basis was recommended, which was used to measure the relative value of data assets.The process of calculating the data asset value index by the use of analytic hierarchy process and the fuzzy comprehensive evaluation method were summarized, and the steps were decomposed.The internal connection and difference between the value and price of the data asset, the value assessment and the pricing of the data asset were demonstrated.The prospect for future research on data asset value management was proposed.

Organizational forms and valuation framework of data factor market

Chuanwei ZOU

2021, 7(4): 28-36. doi:10.11959/issn.2096-0271.2021036

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The organizational forms and valuation framework of data factor market are two important questions in developing and regulating data factor market.To solve these two problems, the idea that clear ownership is not a necessary condition for data factor market to function efficiently was argued.Rather, effective control of data factor is more important.Based the similarities between data factor market and financial system, possible organizational forms of data factor market were suggested.Lastly, a new framework of data factor valuation, DataRank, was proposed, to reflect the subjectivity, time-variance, and externality of data factor value better.

Research and exploration of big data transaction model based on blockchain

Yuan LI, Ning GAO, Jing SUN, Huiqun ZHAO

2021, 7(4): 37-48. doi:10.11959/issn.2096-0271.2021037

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Data is the foundation of the digital economy.However, the issue of data confirmation is currently controversial.As a new type of asset, capitalization standards and pricing standards are still in the exploration stage, and the construction of big data trading platforms is in the ascendant.The status quo and main problems of data right confirmation, pricing and trading were reviewed, and from this condenses, a new big data trading model was proposed, which is the iterative relationship between data right confirmation, pricing and trading.Finally, combined with the technical characteristics of the blockchain, a scheme of big data transaction platform based on the consortium blockchain was put forward, which is from the perspective of individuals and data transaction parties.The platform’s rights and interests protection, pricing mechanism and transaction mode have been designed.

Study on data asset management mechanism based on blockchain technology

Ming ZHAO, Dazhi DONG

2021, 7(4): 49-60. doi:10.11959/issn.2096-0271.2021038

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The blockchain technology can ensure the high security, high privacy and traceability of data asset management.Through research on the current blockchain-based data asset management mechanism, it is concluded that the current management mechanism is only applied in a certain layer of the blockchain framework.In order to solve this problem, a new mechanism of data asset management based on blockchain was proposed.The mechanism combined and applied all layers in the blockchain framework.In addition, this mechanism added a node authority control mechanism at the network layer, realized the consensus mechanism with customizable attributes at the consensus layer, optimized the structure and built indexes to speed up data query efficiency at the data layer, realized intelligent data management and sharing at the smart contract layer, and realized information encryption with customizable encryption algorithms at the transaction layer.Experimental results show that the new mechanism of data asset management based on blockchain improves the efficiency of data query on chain by 2.33 times compared with the traditional mechanism.

A survey of game theory and auction-based data pricing

Xiaowei ZHANG, Dong JIANG, Ye YUAN

2021, 7(4): 61-79. doi:10.11959/issn.2096-0271.2021039

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In the era of big data, with the explosive growth of data, regarding data as a commodity and establishing an efficient data trading market is a important thing.By data trading’s way, profit compensation is provided for data owners, and raw data or services are provided for data consumers, so that data can flow fully freely between data owners and data consumers.However, how to set a reasonable price for the data is necessary.Data pricing based on game theory and auctions was investigated.Different data pricing models under this category were investigated.These models were divided into different types, and the advantages and disadvantages of each model were compared comprehensively.Then, common data trading markets were classified, and the advantages and challenges of different data transaction frameworks in the implementation process were pointed out.A summary of existing data pricing research was made, so that scholars in the field of data pricing can more easily grasp the current research status and the key of data pricing.

Key technologies and research progress of medical knowledge graph construction

Ling TAN, Haihong E, Zemin KUANG, Meina SONG, Yu LIU, Zhengyu CHEN, Xiaoxuan XIE, Jundi LI, Jiawei FAN, Qingchuan WANG, Xiaoyang KANG

2021, 7(4): 80-104. doi:10.11959/issn.2096-0271.2021040

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With the continuous iterative updating of Internet technology, the semantic understanding of massive data is becoming more and more important.Knowledge graph is a kind of semantic network that reveals the relationship between entities.Medicine is one of the most widely used vertical fields of knowledge graph.The construction of medical knowledge graph is also a hot research in the field of artificial intelligence at home and abroad.Starting from the ontology construction of medical knowledge graph, named entity recognition, entity relationship extraction, entity alignment, entity linking, knowledge graph storage and application of knowledge graph were reviewed.The difficulties, existing technologies, challenges and future research directions in the process of constructing medical knowledge graph in recent years were introduced.Finally, the application of knowledge graph and the future development direction of medical knowledge graph were discussed.

A recommender algorithm based on SVD ++model under trust network

Peiwu CHEN, Fangxing SHU

2021, 7(4): 105-116. doi:10.11959/issn.2096-0271.2021041

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Recommender algorithms are usually modeled based on user behavior data.However, the sparseness of explicit behavior data may cause the cold start problem of recommender algorithms.In order to solve the impact of data sparseness and cold-start problems on the effect of recommender algorithms, implicit trust relationship based on user similarity was introduced based on the existing revealed trust relationship, and a new recommender algorithm was designed through the SVD++ implicit semantic model.In order to improve the effect of the algorithm, the neighborhood model was integrated further, and the algorithm score prediction formula and loss function were derived.In the Epinions open source data set, RMSE and MAE were used as test indicators, and comparative experiments were conducted on the entire user set and the cold start user set.The experimental results show that the recommender algorithm can optimize the cold start problem of the original recommender algorithm to a certain extent, and achieve a better rating prediction accuracy.

A sort-last architecture based parallel volume visualization algorithm for unstructured grid

Liang FAN, Xiaorong ZHANG, Yadong WU, Cheng CHEN, Fang WANG

2021, 7(4): 117-129. doi:10.11959/issn.2096-0271.2021042

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Volume rendering is one of significant unstructured grid data volume visualization methods.However, existing serial algorithms for unstructured grid volume rendering are inefficient and cannot meet large-scale data volume visualization needs.Therefore, parallel volume rendering algorithm research has become a trend in high-performance volume visualization program design.A sort-last based parallel volume rendering algorithm for unstructured gird was proposed.Firstly, a parallel KD-tree algorithm for volumetric data split was designed.Secondly, each process calculated volume rendering images with independent visualization pipeline, and the final result was synthesized with tree composite strategy.Finally, a two-level LOD model was leveraged to optimize interactive experience.The experimental results show that the proposed algorithm can be well applied to large-scale unstructured grid data volume visualization, and all interaction delays are in milliseconds, which meets real-time interaction needs.

Analysis of gender differences in the brain based on deep learning

Jingxi WEN, Hufei YU, Jiang XIN, Yan TANG

2021, 7(4): 130-140. doi:10.11959/issn.2096-0271.2021043

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Deep learning is widely used in brain related research.A deep learning model was constructed to classify the fraction anisotropy of the diffusion tensor imaging data.And the important features of different genders were extracted through the deep learning feature visualization method.Finally the visualization results were analyzed based on voxels.The results show that the proposed model can accurately predict gender and achieve a classification accuracy of 96.2%.In the visualized results, it is found that there are obvious differences between the brains of men and women.The brain regions with differences are mainly manifested in the corpus callosum, inferior parietal lobule and basal ganglia.These brain regions reveal that the brain differences between men and women may be related to exercise ability, mathematical operations, body image perception, and emotional changes.

Analysis and utilization framework of electronic medical records under the background of smart hospital construction

Liangchen XU, Chonghui GUO

2021, 7(4): 141-156. doi:10.11959/issn.2096-0271.2021044

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As the core medical big data, electronic medical record data has become the core foundation of smart hospital construction, and the research on the analysis and utilization of electronic medical records is of great significance.In order to promote the analysis and utilization of electronic medical records, an integrated research framework for the generation, analysis and utilization of electronic medical records was proposed, the connotation of electronic medical records was expounded, the relationship between electronic medical record systems and other medical information systems was analyzed, and the analysis and mining process of electronic medical records were sorted out.The application of electronic medical record analysis was also summarized from three perspectives: computer-aided diagnosis, treatment recommendation and management support.The impact of electronic medical record analysis on the relevant classification of smart hospital was also discussed.Finally, the existing problems in the analysis and utilization of electronic medical records were analyzed from the data level, model level and application level, and corresponding opinions and suggestions were given to provide references for the analysis and utilization of electronic medical records and the construction of smart hospital.

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