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Big data technologies forward-looking Hong MEI, Xiaoyong DU, Hai JIN, Xueqi CHENG, Yunpeng CHAI, Xuanhua SHI, Xiaolong JIN, Yasha WANG, Chi LIU Big Data Research    2023, 9 (1): 1-20.   DOI: 10.11959/j.issn.2096-0271.2023009 Abstract （2613）   HTML （977）    PDF（pc） （1087KB）（1545）       Knowledge map    Save Major countries in the world attach great importance to the development of big data technology.China also puts big data as a national strategy, of great significance to develop in the long run.Big data technologies include data collection, transmission, management, processing, analysis, and application, forming a data life cycle as well as the data governance related to each procedure.Big data management, processing, analysis, and governance in four areas were seleceted, to identify the gap between China and the world.On the other hand, driven by diverse successful big data applications, the system architecture of computing technology is being restructured.From “computation-centric” to “data-centric”, fundamental computing theories and core technologies need to be redesigned, therefore a new type of big data system technology is becoming an important research direction.Against this background, four technical challenges and ten future development trends of big data technologies were aimed at identifying. Reference | Related Articles | Metrics

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Construction, reasoning and applications of event graphs Zhilei HU, Xiaolong JIN, Jianyun CHEN, Guanli HUANG Big Data Research    2021, 7 (3): 80-96.   DOI: 10.11959/j.issn.2096-0271.2021027 Abstract （2212）   HTML （331）    PDF（pc） （1381KB）（2052）       Knowledge map    Save In recent years, the construction technology of knowledge graphs have been greatly developed, and the constructed knowledge graphs have been applied to many fields.On this basis, the researchers turned their attention from the knowledge graph to the event graph.The event graph takes the event as the core and accurately describes the event information and the relationship between the events.The key technologies of event graphs construction, reasoning and applications were summarized, including event extraction, event information completion, event relationship inference and event prediction.Finally, the specific application scenarios of the event graphs were given, and the future research trends were prospected in view of the challenges existing in the event graph research. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics

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Progress, challenges and research trends of reasoning in multi-hop knowledge graph based question answering Huifang DU, Haofen WANG, Yinghui SHI, Meng WANG Big Data Research    2021, 7 (3): 60-79.   DOI: 10.11959/j.issn.2096-0271.2021026 Abstract （1941）   HTML （381）    PDF（pc） （1744KB）（2083）       Knowledge map    Save Recently, knowledge graph based question answering has been widely used in many fields such as medical care, finance, and government affairs.Users are no longer satisfied with question answering service of single-hop entity attributes, but want service which can handle complex multi-hop question.In order to accurately and deeply understand multi-hop questions, various types of reasoning methods have been proposed.The latest research methods of multi-hop knowledge graph based question answering were systematically introduced, as well as related datasets and evaluation metrics.These Table and Figures | Reference | Related Articles | Metrics

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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 Big Data Research    2021, 7 (4): 80-104.   DOI: 10.11959/issn.2096-0271.2021040 Abstract （1897）   HTML （300）    PDF（pc） （1542KB）（2232）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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Research advances on privacy protection of federated learning Jianzong WANG, Lingwei KONG, Zhangcheng HUANG, Linjie CHEN, Yi LIU, Chunxi LU, Jing XIAO Big Data Research    2021, 7 (3): 130-149.   DOI: 10.11959/j.issn.2096-0271.2021030 Abstract （1809）   HTML （317）    PDF（pc） （1923KB）（2843）       Knowledge map    Save To this end, many laws and regulations on privacy protection have been introduced, and the phenomenon of data-island has become a major bottleneck hindering the development of big data and artificial intelligence technology.Federated learning has received widespread attention to break this phenomenon.Started with the historical development of federated learning, the definition, and architecture and classification of federated learning, the advantages of federated learning in privacy protection domainwere introduced.At the same time, various attack methods and their classification aboutfederated learning were introduced in detail.The classification of various encryption algorithms in federated learning were summarized.In conclusion, the contribution of federated learning in privacy protection and security mechanism were summarized and the new challenges in these domains were proposed. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics

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Threats and defenses of federated learning: a survey Jianhan WU, Shijing SI, Jianzong WANG, Jing XIAO Big Data Research    2022, 8 (5): 12-32.   DOI: 10.11959/j.issn.2096-0271.2022038 Abstract （1762）   HTML （256）    PDF（pc） （2537KB）（1951）       Knowledge map    Save With the comprehensive application of machine learning technology, data security problems occur from time to time, and people’s demand for privacy protection is emerging, which undoubtedly reduces the possibility of data sharing between different entities, making it difficult to make full use of data and giving rise to data islands.Federated learning (FL), as an effective method to solve the problem of data islands, is essentially distributed machine learning.Its biggest characteristic is to save user data locally so that the models’ joint training process won’t leak sensitive data of partners.Nevertheless, there are still many security risks in federated learning in reality, which need to be further studied.The possible attack means and corresponding defense measures were investigated in federal learning comprehensively and systematically.Firstly, the possible attacks and threats were classified according to the training stages of federal learning, common attack methods of each category were enumerated, and the attack principle of corresponding attacks was introduced.Then the specific defense measures against these attacks and threats were summarized along with the principle analysis, to provide a detailed reference for the researchers who first contact this field.Finally, the future work in this research area was highlighted, and several areas that need to be focused on were pointed out to help improve the security of federal learning. Table and Figures | Reference | Related Articles | Metrics

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Features and transaction modes of data products in data markets Lihua HUANG, Yifan DOU, Mengke GUO, Qifeng TANG, Gen LI Big Data Research    2022, 8 (3): 3-14.   DOI: 10.11959/j.issn.2096-0271.2022045 Abstract （1297）   HTML （284）    PDF（pc） （1700KB）（1657）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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Knowledge graph reasoning: modern methods and applications Wenguang WANG Big Data Research    2021, 7 (3): 42-59.   DOI: 10.11959/j.issn.2096-0271.2021025 Abstract （1262）   HTML （211）    PDF（pc） （2680KB）（1352）       Knowledge map    Save Knowledge reasoning over knowledge graph aims to discover new knowledge according to the existing knowledge.It is a pivotal technology to realize the human reasoning and decision-making ability of machine.The modern methods of knowledge reasoning over knowledge graph were studied systematically.And the methods based on vector representations with a unified framework were introduced, including the methods based on embedding into Euclidean space and hyperbolic space, and based on deep learning methods such as convolution neural network, capsule network, graph neural network, etc.Simultaneously, the applications of knowledge reasoning in various technical fields and industries were presented, and the existing challenges and opportunities were pointed out as well. Table and Figures | Reference | Related Articles | Metrics

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Assessment and pricing of data assets:research review and prospect Chuanru YIN, Tao JIN, Peng ZHANG, Jianmin WANG, Jiayi CHEN Big Data Research    2021, 7 (4): 14-27.   DOI: 10.11959/issn.2096-0271.2021035 Abstract （1173）   HTML （216）    PDF（pc） （1814KB）（1615）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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Survey on federated recommendation systems Zhitao ZHU, Shijing SI, Jianzong WANG, Jing XIAO Big Data Research    2022, 8 (4): 105-132.   DOI: 10.11959/j.issn.2096-0271.2022032 Abstract （1088）   HTML （137）    PDF（pc） （2663KB）（1164）       Knowledge map    Save In the federated learning (FL) paradigm, the original data are stored in independent clients while masked data are sent to a central server to be aggregated, which proposes a novel design approach to numerous domains.Given the wide application of recommendation systems (RS) in diverse domains, combining RS with FL techniques has been gaining momentum to reduce the computational cost, do cross-domain recommendation and protect users’ privacy while maintaining recommendations performance as traditional RS.The federated learning-based recommendation systems in recent years were comprehensively summarized.The difference between traditional and federated recommendation systems was analyzed, and the main research direction and progress of federated recommendation systems were demonstrated with comparison and analysis.Firstly, the traditional recommendation systems and their bottleneck were summarized.Then the federated learning paradigm was introduced.Furthermore, the advantages of combining federated learning with recommendation systems were depicted in two aspects: privacy protection and usage of multi-domain user information, along with the technical challenges during the combination.At the same time, the existing deployment of federated recommendation systems was illustrated in detail.Finally, future research on federated recommendation systems was prospected and summarized. Table and Figures | Reference | Related Articles | Metrics

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Recognition method of accounting fraud risk based on financial knowledge graph Qiang CHEN, Shiya DAI Big Data Research    2021, 7 (3): 116-129.   DOI: 10.11959/j.issn.2096-0271.2021029 Abstract （1024）   HTML （207）    PDF（pc） （2017KB）（1358）       Knowledge map    Save Since the accounting risk events exhibit complexity increasingly and occur frequently, a method merged by industrial knowledge and financial knowledge graph was proposed to recognize and prevent commercial bank's accounting risk more precisely.Based on the financial knowledge graph of account transaction, deep graph connected risk features were extracted via various graph analysis and mining technologies.Combining the graph features with industrial knowledge, 249 single rules and 425 assembled rules were constructed to form a more affluent and flexibly configurable anti-fraud strategy system, which was then applied to verify commercial bank's current accounts and select the high suspicious ones.The experimental results show that the risk recognition accuracy rate of the intelligent strategy is much higher than the traditional one and reaches up to 85% above, which significantly promotes the efficiency of the accounting risk verification. Table and Figures | Reference | Related Articles | Metrics

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A review and comparative analysis of domestic and foreign research on big data pricing methods Nan LIU, Xuejing HAO, Yuhong CHEN Big Data Research    2021, 7 (6): 89-102.   DOI: 10.11959/j.issn.2096-0271.2021063 Abstract （962）   HTML （179）    PDF（pc） （1377KB）（1032）       Knowledge map    Save Due to the value characteristics of big data itself, the problem of data pricing is complicated.Although researchers have conducted a lot of research on this, most of them have a single angle and lack a certain practical application.In view of this, the big data pricing methods were reviewed, five types of pricing were sorted out: cost-oriented, market-oriented, demand-oriented, profit-oriented, and life-cycle-based pricing.The advantages and disadvantages of the six mainstream pricing methods were compared: cost method, agreement pricing, market method, income method, quality-based and query-based pricing.Finally, through the analysis of the big data pricing process, the characteristics of the different pricing methods were further revealed, and the data pricing direction was forecasted.The article aims to provide some reference for future related research. Table and Figures | Reference | Related Articles | Metrics

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Temporal knowledge graph completion:methods and progress Yuming SHEN, Jianfeng DU Big Data Research    2021, 7 (3): 30-41.   DOI: 10.11959/j.issn.2096-0271.2021024 Abstract （943）   HTML （122）    PDF（pc） （1275KB）（1330）       Knowledge map    Save Temporal knowledge graph (TKG) are obtained by adding the time information of real-world knowledge to classical knowledge graphs.Recently, TKG completion has drawn much attention and become a hot topic in research.Two main methodologies for TKG completion were summarized, one based on symbolic logic whereas and the other based on knowledge representation learning.The pros and cons of these two different methodologies were discussed, highlighting some directions for enhancing TKG completion in future research.Also, seven benchmark datasets for TKG completion and evaluation results of several typical models on the benchmark datasets were introduced. Table and Figures | Reference | Related Articles | Metrics

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Value chain model of data governance and its application on data governance regulation analysis Keman HUANG, Xiaoyong DU Big Data Research    2022, 8 (4): 3-16.   DOI: 10.11959/j.issn.2096-0271.2022062 Abstract （919）   HTML （351）    PDF（pc） （1444KB）（973）       Knowledge map    Save Cultivating the data marketplace is an important mechanism to achieve the value of big data.The prosperity of such a data marketplace needs a sustainable and healthy data service ecosystem.A data governance value chain model was developed to identify the primary and support activities for data value release.Then the data service ecosystem model was implemented accordingly to distinguish different stakeholders and their core functions that a data marketplace should have.Using the developed data governance value chain model and data service ecosystem model, the data dovernance regulation was analyzed systematically, aiming at providing suggestions to promote the growth of the data marketplace. Table and Figures | Reference | Related Articles | Metrics

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Large scale pre-trained knowledge graph model and e-commerce application Huajun CHEN, Wen ZHANG, Chi-Man WONG, Ganqiang YE, Bo WEN, Wei ZHANG Big Data Research    2021, 7 (3): 97-115.   DOI: 10.11959/j.issn.2096-0271.2021028 Abstract （909）   HTML （138）    PDF（pc） （2518KB）（951）       Knowledge map    Save In recent years, knowledge graph has been widely applied to organize data in a uniform way and enhance many tasks that require knowledge.For example, it has been widely used in the field of e-commerce.However, such knowledge services usually include tedious data selection and model design for knowledge infusion, which might bring inappropriate results.Thus, to solve this problem, the method of first pre-training then providing knowledge vector service was put forward, and a pre-trained knowledge graph model (PKGM) was proposed for our billionscale e-commerce product knowledge graph, providing item knowledge services in a uniform way for embeddingbased models without accessing triple data in the knowledge graph.PKGM was tested in three knowledge-related tasks including item classification, same item identification, and recommendation.Experimental results show PKGM successfully improves the performance of each task. Table and Figures | Reference | Related Articles | Metrics

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A survey on information extraction technology based on remote sensing big data Weiquan LIU, Cheng WANG, Yu ZANG, Qian HU, Shangshu YU, Baiqi LAI Big Data Research    2022, 8 (2): 28-57.   DOI: 10.11959/j.issn.2096-0271.2022014 Abstract （907）   HTML （229）    PDF（pc） （9092KB）（448）       Knowledge map    Save With the rapid development of remote sensing technology, our country has established a relatively complete space remote sensing and flexible and diverse aerial remote sensing data acquisition system.Remote sensing big data is mainly based on massive remote sensing data, integrating other multi-source remote sensing data, using big data thinking and methods, and discovering knowledge laws and high-value information in massive data.Firstly, the research work of information extraction technology based on remote sensing big data was reviewed in recent years.Secondly, the development history of remote sensing information extraction technology was expounded from three aspects: remote sensing target detection, remote sensing surface object segmentation, and remote sensing change detection.Finally, the information extraction technology based on remote sensing big data was sorted out, summarized and prospected. Table and Figures | Reference | Related Articles | Metrics

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Opportunities and challenges of geo-spatial information science from the perspective of big data Deren LI, Guo ZHANG, Yonghua JIANG, Xin SHEN, Weiling LIU Big Data Research    2022, 8 (2): 3-14.   DOI: 10.11959/j.issn.2096-0271.2022012 Abstract （842）   HTML （172）    PDF（pc） （1585KB）（539）       Knowledge map    Save The era of big data has arrived, and it has penetrated every aspect of human life.As the geo-spatial information science spawned by the intersection of earth sciences and information sciences, the advent of the era of big data provides it with richer prosperous data protection, but also brings new challenges in data storage, management, analysis, and mining, and even caused a certain degree of “data explosion”.From the perspective of big data, the bottlenecks and challenges in the four core areas of geographic information systems, smart cities, remote sensing big data, and spatial data mining were sorted out.And it was pointed out that geo-spatial information science can provide more accurate and real-time spatial information frameworks and more intelligent and more efficient information processing methods for geoscience research, serving intelligent cities, smart earth construction, and sustainable development in the era of big data.Moreover, in the era of big data, the development of geo-spatial information science is facing the double test of software and hardware levels. Reference | Related Articles | Metrics

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Digital economics in metaverse: state-of-the-art, characteristics, and vision Chenhuizi WANG, Wei CAI Big Data Research    2022, 8 (3): 140-150.   DOI: 10.11959/j.issn.2096-0271.2022048 Abstract （840）   HTML （166）    PDF（pc） （1379KB）（1075）       Knowledge map    Save 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. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics

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Developing Data Factor Market Big Data Research    2022, 8 (3): 1-2.   DOI: 10.11959/j.issn.2096-0271.2022045-1 Abstract （803）   HTML （222）    PDF（pc） （888KB）（467）       Knowledge map    Save Reference | Related Articles | Metrics

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Data-Commerce-Ecosystem: data goods, data businessman and data commerce Yazhen YE, Yangyong ZHU Big Data Research    2023, 9 (1): 111-125.   DOI: 10.11959/j.issn.2096-0271.2023003 Abstract （786）   HTML （160）    PDF（pc） （1288KB）（584）       Knowledge map    Save With the progress in the development of the data factor market, the concept of Data-Commerce-Ecosystem (DCE) has attracted wide attention.However, there has been little discussion on the connotation of DCE as well as its role and responsibilities in the modern-day economy, which hinders the formation of a data trade ecosystem.Possible categories of contemporary data goods, data businessmen, and data commerce, the proposed definitions of said concepts were discussed.Information goods, digital goods, and data goods were incorporated into the concept of data goods.Data businessmen were categorized into three groups based on their different commerce models, which were data suppliers, data service providers, and data commodity traders.Several DCE models were summarized, which were the self-produceand-market model, operation platform agent model, and data marketplace model.These discussions enrich the connotation of DCE and in turn provide theoretical support for the development of the data factor market. Table and Figures | Reference | Related Articles | Metrics

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Rhythm dancer: 3D dance generation by keymotion transition graph and pose-interpolation network Yayun HE, Junqing PENG, Jianzong WANG, Jing XIAO Big Data Research    2023, 9 (1): 23-37.   DOI: 10.11959/j.issn.2096-0271.2023004 Abstract （756）   HTML （89）    PDF（pc） （3750KB）（414）       Knowledge map    Save 3D dance is an indispensable form of virtual humans in the metaverse.It organically combines music and dance art, which greatly increases the interest in the metaverse.Previous work usually treats it as a simple sequence generation task, but it is difficult to match the dance movements with the music beat perfectly and the quality of long sequence dance generation is difficult to be guaranteed.Inspired by the process by which humans learn to dance, a novel 3D dance framework “Rhythm Dancer”to solve the above problems was proposed.The framework first uses VQ-VAE-2 to encode and quantify the dances in a hierarchical way, which effectively improves the quality of dance generation.Then, a key movement transition map was created using the core dance movements on the rhythm points, which not only ensures that the generated dance movements fit with the music beat, but also increases the diversity of dance movements.To ensure smooth and natural connections between the core dance moves, a poseinterpolation network was proposed to learn the transition movements between key moves.Extensive experiments demonstrate that the framework not only avoids the instability and uncontrollability problems of long sequence generation, but also achieves a higher match between dance movements and music rhythms, reaching state-of-the-art results. Table and Figures | Reference | Related Articles | Metrics

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Research and exploration of big data transaction model based on blockchain Yuan LI, Ning GAO, Jing SUN, Huiqun ZHAO Big Data Research    2021, 7 (4): 37-48.   DOI: 10.11959/issn.2096-0271.2021037 Abstract （744）   HTML （161）    PDF（pc） （1533KB）（929）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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Applications of remote sensing big data in highway transportation Shenggu YUAN, Lun LUO, Ronggang GUO, Hengbin MAO, Fang WANG, Hongyue CAI, Heping XIAO Big Data Research    2022, 8 (2): 103-119.   DOI: 10.11959/j.issn.2096-0271.2022011 Abstract （716）   HTML （131）    PDF（pc） （6679KB）（750）       Knowledge map    Save The rise of remote sensing big data applications had a profound impact on the transportation industry, and has played an active role in transportation planning, construction, management and maintenance and other aspects.Firstly, the connotation and characteristics of traffic remote sensing big data were introduced, and the application status of remote sensing big data was summarized in the field of highway traffic and transportation.Secondly, combined with the relevant work carried out by transportation departments based on remote sensing big data in recent years, the typical applications of remote sensing big data in intelligent extraction and analysis of traffic highway damage, highway construction and planning analysis, and intelligent maintenance of highway were mainly introduced.Finally, the development trend and future prospects of traffic remote sensing big data were prospected. Table and Figures | Reference | Related Articles | Metrics

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Authenticating and licensing architecture of data rights in data trade Qifeng TANG, Zhiqing SHAO, Yazhen YE Big Data Research    2022, 8 (3): 40-53.   DOI: 10.11959/j.issn.2096-0271.2022029 Abstract （706）   HTML （113）    PDF（pc） （1417KB）（629）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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A survey of game theory and auction-based data pricing Xiaowei ZHANG, Dong JIANG, Ye YUAN Big Data Research    2021, 7 (4): 61-79.   DOI: 10.11959/issn.2096-0271.2021039 Abstract （692）   HTML （83）    PDF（pc） （1656KB）（969）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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Enlightenment of open access to public data in the European Union Qun ZHANG, Zhuo YIN, Hao YU, Weizhong WANG, Xiaojie JIA Big Data Research    2022, 8 (6): 143-152.   DOI: 10.11959/j.issn.2096-0271.2022047 Abstract （687）   HTML （59）    PDF（pc） （1264KB）（626）       Knowledge map    Save Open access to public data contributes to the high-quality development of the digital economy.In the early stage, China actively introduced relevant policies to guide the openness and utilization of public data, and many local regulations issued relevant local rules and regulations.But the national level has not yet issued relevant rules and regulations for the openness and utilization of public data.Compared with our country, the European Union is continuously issuing and revising directives related to open access to public data, to promote technological innovation in the field of the digital economy.The relevant practices of open access to public data in China were sorted out, and the main directions and characteristics of the EU’s open data and public sector information reuse directives were analyzed.Combing with China’s situation, the relevant enlightenments and suggestions on the open access to public data in China were put forward.Hope that it will be useful to further improve the open access policies, regulations, and mechanism of public data, promote the deep sharing and orderly opening of public data in China. Reference | Related Articles | Metrics

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Study on data asset management mechanism based on blockchain technology Ming ZHAO, Dazhi DONG Big Data Research    2021, 7 (4): 49-60.   DOI: 10.11959/issn.2096-0271.2021038 Abstract （680）   HTML （110）    PDF（pc） （1423KB）（880）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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Research on the development path and countermeasures of data element value Yunlong YANG, Liang ZHANG, Xulei YANG Big Data Research    2023, 9 (6): 100-109.   DOI: 10.11959/j.issn.2096-0271.2022080 Abstract （673）   HTML （106）    PDF（pc） （2022KB）（863）       Knowledge map    Save Based on the development of data element marketization at home and abroad, the development path and characteristics of data element value in foreign countries were expounded.The current situation of China's data element market in terms of transaction market and application scenarios was summarized.In view of the current development of China's data element market, combined with China's data element market environment and development characteristics, through the construction of a data element market model with Chinese characteristics, we can speed up the release of data element value. Table and Figures | Reference | Related Articles | Metrics

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Issues faced by the determination of data ownership and solutions Bo HE Big Data Research    2021, 7 (4): 3-13.   DOI: 10.11959/issn.2096-0271.2021034 Abstract （672）   HTML （116）    PDF（pc） （1009KB）（838）       Knowledge map    Save 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. Reference | Related Articles | Metrics

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Overview of observational data-based time series causal inference Zefan ZENG, Siya CHEN, Xi LONG, Guang JIN Big Data Research    2023, 9 (4): 139-158.   DOI: 10.11959/j.issn.2096-0271.2022059 Abstract （660）   HTML （67）    PDF（pc） （2614KB）（1486）       Knowledge map    Save With the increase of data storage and the improvement of computing power,using observational data to infer time series causality has become a novel approach.Based on the properties and research status of time series causal inference,five observational data-based methods were induced,including Granger causal analysis,information theory-based method,causal network structure learning algorithm,structural causal model-based method and method based on nonlinear state-space model.Then we briefly introduced typical applications in economics and finance,medical science and biology,earth system science and other engineering fields.Further,we compared the advantages and disadvantages and analyzed the ways for improvement of the five methods according to the focus and difficulties of time series causal inference.Finally,we looked into the future research directions. Table and Figures | Reference | Related Articles | Metrics