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Privacy preservation in big data:a survey Binxing FANG, Yan JIA, Aiping LI, Rong JIANG Big Data Research    2016, 2 (1): 1-18.   DOI: 10.11959/j.issn.2096-0271.2016001 Abstract （3888）   HTML （808）    PDF（pc） （1420KB）（4600）       Knowledge map    Save Privacy disclosure issue becomes more and more serious due to big data analysis.Privacy-preserving techniques should be conductive to the big data applications while preserving data privacy.Since big data has the characteristics of huge scale,numerous sources and dynamic update,most traditional privacy preserving technologies are not suitable any more.Therefore,the concept of privacy and life cycle protection model of big data era were introduced firstly.Technical state of big data privacy preservation was elaborated from the points of view of four stages in big data life cycle,i.e.data publishing,storage,analysis and use.The relative merits and scope of application of each technology were investigated as well.Finally,some important direction and tendency of privacy preservation technologies for big data were suggested. Table and Figures | Reference | Related Articles | Metrics Cited: Baidu(7)

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Data wrangling:a key technique of data governance Xiaoyong DU, Yueguo CHEN, Ju FAN, Wei LU Big Data Research    2019, 5 (3): 13-22.   DOI: 10.11959/j.issn.2096-0271.2019020 Abstract （3322）   HTML （1165）    PDF（pc） （1421KB）（2260）       Knowledge map    Save Data is an important resource for governments,businesses and institutions.Data governance focuses on many aspects of effective use of data resources,such as data asset,data management,data sharing,and data privacy.A key techniquedata wrangling-in data governance from the perspective of data management was explored.The key technologies of data wrangling based on data owners and direct users-industry users were emphasized,including data structure processing,data quality assessment and data cleaning,data normalization,data fusion and extraction,data publishing and sharing,etc.Finally,some thoughts on strengthening the research on data organization were put forward. Table and Figures | Reference | Related Articles | Metrics

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Data management capability maturity model Bing LI,Junzhi BIN Big Data Research    2017, 3 (4): 29-36.   DOI: 10.11959/j.issn.2096-0271.2017039 Abstract （2935）   HTML （683）    PDF（pc） （1203KB）（5681）       Knowledge map    Save To promote the continuous development of big data and improve the government and enterprises’ awareness of data management,the organizational data management of the eight process areas were extracted by analyzing and summarizing the data management capabilities,combining with data lifecycle management at all stages of the characteristics and the theory from domestic and abroad.Otherwise,each capability was divided into two process areas and development levels,introduced the functions and formulated the standardization of assessment. Table and Figures | Reference | Related Articles | Metrics Cited: Baidu(1)

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Big data governance in governments:a new form of the government administration Menghui YANG, Xiaoyong DU Big Data Research    2020, 6 (2): 3-18.   DOI: 10.11959/j.issn.2096-0271.2020010 Abstract （2870）   HTML （1275）    PDF（pc） （1443KB）（2244）       Knowledge map    Save Exploring big data governance in governments becomes an important issue for government sectors.By using literature survey,this study made a comprehensive analysis of big data practice in governments.Results shown that the big data application projects in governments were still at the early stage.The concept of big data governance in governments was traced.The concept of big data governance in governments inherited the governance theories and administrative management means in the public management and the administrative management areas,and also borrowed the frameworks and approaches,from IT governance,data governance,and big data governance in corporate governance.The concept and characteristics of big data governance in governments were explained.New challenges,new goals and new mechanisms of the big data governance in governments were proposed.It was pointed out that big data governance in governments is a new form of government management. Table and Figures | Reference | Related Articles | Metrics

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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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Architecture,challenges and applications of edge computing Linzhe LI, Peilei ZHOU, Peng CHENG, Zhiguo SHI Big Data Research    2019, 5 (2): 3-16.   DOI: 10.11959/j.issn.2096-0271.2019010 Abstract （2525）   HTML （841）    PDF（pc） （1661KB）（2670）       Knowledge map    Save Edge computing is a new type of computing models that performs computing tasks at the edge of the network.Compared with cloud computing,it can respond to user’s needs more quickly and reliably.Starting from the shortcomings of cloud computing,the concept and general architecture of edge computing were illustrated,and then two reference frameworks proposed by industry alliances were elaborated.Four challenges of edge computing and their latest research progress were summarized.With the development of theory and technology related to edge computing,it will become a key technology to promote the upgrade of Internet of things (IoT) services.For this reason,two applications of edge computing in manufacturing and security monitoring were introduced. Table and Figures | Reference | Related Articles | Metrics

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Big data standards system Qun ZHANG,Dongya WU,Jinghua ZHAO Big Data Research    2017, 3 (4): 11-19.   DOI: 10.11959/j.issn.2096-0271.2017037 Abstract （2381）   HTML （153）    PDF（pc） （1122KB）（1980）       Knowledge map    Save With the development of big data,standardization involves more and more content,and the objects are becoming more and more complex.The status of national and international big data standardization work was systematically analyzed.In combination with the national strategy of "Platform for the Development of Big Data" and the Thirteenth Five-Year Plan of National Economic and Social Development of the People's Republic of China,as well as the demand for big data standardization,the problems of national big data standardization were pointed out,big data reference architecture and standards system was proposed,and suggestions for future work were given. Table and Figures | Reference | Related Articles | Metrics

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Process and methods of clinical big data mining based on electronic medical records Tong RUAN, Ju GAO, Donglei FENG, Xiyuan QIAN, Ting WANG, Chenglin SUN Big Data Research    2017, 3 (5): 83-98.   DOI: 10.11959/j.issn.2096-0271.2017054 Abstract （2280）   HTML （286）    PDF（pc） （2153KB）（3873）       Knowledge map    Save Electronic medical records from hospitals record the patient's disease,diagnosis and treatment information.It forms the basis of clinical data.Mining such data can assist doctors in clinical research and clinical diagnosis and treatment.Firstly,challenges encountered in the process of big data mining on EMR were raised,then the core process was summarized.The process includes tasks such as clinical data integration,the construction of clinical specialist disease database based on knowledge graph,the quality assessment methods on EMR,and comparative effectiveness and risk prediction of diseases as the core of clinical big data applications.A solution for each task was proposed,and the experimental results were given.Finally,the future directions of technologies and applications of big data mining on healthcare were presented. Table and Figures | Reference | Related Articles | Metrics Cited: Baidu(1)

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Defining data assets based on the attributes of data Yangyong ZHU, Yazhen YE Big Data Research    2018, 4 (6): 65-76.   DOI: 10.11959/j.issn.2096-0271.2018062 Abstract （2253）   HTML （232）    PDF（pc） （1710KB）（1562）       Knowledge map    Save In the background of big data now,it has been widely recognized that data is the key factor of digital economy.Therefore,it is necessary to understand the meaning behind of the definitions of information assets,digital assets and data assets.A survey of information assets,digital assets,and data assets was given.The physical attributes,existence attributes and information attributes of data and data assets were discussed.Based on these attributes,information assets,digital assets and data assets were merged into data assets.Data assets were defined as valuable,measurable and accessible data resources in cyberspace owned by an accounting subject.According to the definition and attributes of data assets,data assets have the characteristics of both tangible assets and intangible assets,current assets and long-term assets.Therefore,data assets should be considered as a new category of assets. Table and Figures | Reference | Related Articles | Metrics

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Techniques of big data security from the perspective of life cycle management Shudong LI, Yan JIA, Xiaobo WU, Aiping LI, Xiaodong YANG, Dawei ZHAO Big Data Research    2017, 3 (5): 3-19.   DOI: 10.11959/j.issn.2096-0271.2017047 Abstract （2228）   HTML （166）    PDF（pc） （1537KB）（2494）       Knowledge map    Save The big data security problems during the process of data production,storage,use,transmission,sharing and destruction have been explored.Firstly,the development strategies of big data security worldwide were summarized.Second,from the perspective of the whole life cycle management,the current technical research and the progress of big data security protection were introduced by focusing on its collection,storage,transport,use and open sharing,destruction and management strategy.Finally,some new problems that need to be solved in the future research of big data security were discussed. Table and Figures | 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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Research review of federated learning algorithms Jianzong WANG, Lingwei KONG, Zhangcheng HUANG, Linjie CHEN, Yi LIU, Anxun HE, Jing XIAO Big Data Research    2020, 6 (6): 64-82.   DOI: 10.11959/j.issn.2096-0271.2020055 Abstract （2199）   HTML （360）    PDF（pc） （1224KB）（2967）       Knowledge map    Save In recent years,federated learning has been proposed and received widespread attention to overcome data isolated island challenge.Federated learning related researches were adopted in areas such as financial field,healthcare domain and smart city related application.Federated learning concept was introduced into three different layers.The first layer introduced the definition,architecture,classification of federated learning and compared the federated learning with traditional distributed learning.The second layer presented comparison and analysis of federated learning algorithms from machine learning and deep learning aspects.The third layer separated federated learning optimization algorithms into three aspects to optimize federated learning algorithm through reducing communication cost,selecting proper clients and different aggregation method.Finally,the current research status and three main challenges on communication,heterogeneity of system and data to be solved were concluded,and the future prospects in federated learning domain were proposed. Table and Figures | Reference | Related Articles | Metrics

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Blockchain based supply-chain finance service platform Xiaofeng MA, Mingxiao DU, Wenbing YU, Yi WANG Big Data Research    2018, 4 (1): 13-21.   DOI: 10.11959/j.issn.2096-0271.2018002 Abstract （2109）   HTML （139）    PDF（pc） （1527KB）（1411）       Knowledge map    Save Blockchain has the characteristics of decentralization,stability,security,and non-modifiability.With the further improvement and application of the blockchain technology,it will be connected with the traditional industries such as finance,medical treatment and logistics.This will radically change the existing operation modes in some industries and restructure a value network.An application of blockchain in the field of supply-chain finance was tried,and a supplychain finance service platform based on the blockchain technology was constructed.The platform combines blockchain technology with other traditional systems,providing a more convenient way of financing for all parties in the supply-chain,improving the transparency,traceability and security of the supply-chain. Table and Figures | Reference | Related Articles | Metrics

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Design scheme of massive traffic data real-time access based on HBase and ElasticSearch Changqing DONG, Nver REN, Qingyu ZHANG, Yujing TIAN Big Data Research    2017, 3 (1): 80-89.   DOI: 10.11959/j.issn.2096-0271.2017010 Abstract （1943）   HTML （374）    PDF（pc） （1487KB）（4227）       Knowledge map    Save Traffic data has the characteristics of massive and real-time,and its massive data acquisition,storage and retrieval has become a key issue in the vehicle remote monitoring platform.According to the study of these problems,the cluster technology of LVS was used to solve the data acquisition load balance,the queue cache model was used to solve I/O delay,and HBase distributed data storage scheme was used to solve the massive data storage.HBase integration ElasticSearch,which was aimed to solve the real-time online data processing problems of Hadoop,was designed to build a hierarchical index.Through the design and implementation of the key technologies,the number of vehicle monitoring had been promoted from 400 to 1 million,online query speed increased about 10 to 20 times based on PB level data.The results verified the efficiency of the scheme. Table and Figures | Reference | 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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Edge intelligence:a new nexus of edge computing and artificial intelligence Zhi ZHOU, Shuai YU, Xu CHEN Big Data Research    2019, 5 (2): 53-63.   DOI: 10.11959/j.issn.2096-0271.2019013 Abstract （1940）   HTML （270）    PDF（pc） （2589KB）（1865）       Knowledge map    Save Artificial intelligence (AI) and edge computing (EC) represent two of today’s most popular technologies.There is a great potential to coordinate these two emerging techniques to facilitate the further advent of both sides.Through three research cases,the profound benefits were demonstrated when AI and EC synergize.Specifically,from the perspective of EC for AI,to efficiently run deep learning at the network edge,a collaborative and on-demand deep neural network (DNN) co-inference framework with device-edge synergy was proposed.By applying DNN partitioning and right-sizing,it minimizes the inference latency under target accuracy.On the other hand,from the perspective of AI for EC,for the dynamical placement of edge computing services,two methods were proposed:an online-learning based adaptive service migration strategy and a factor graph model driven predictive service migration technique. Table and Figures | Reference | Related Articles | Metrics

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Survey on industrial big data Jianmin WANG Big Data Research    2017, 3 (6): 3-14.   DOI: 10.11959/j.issn.2096-0271.2017057 Abstract （1913）   HTML （226）    PDF（pc） （3414KB）（2860）       Knowledge map    Save Industrial big data is a collection of data in the industry application,which consist of business data in the information system,machine data in the industrial IoT system,and related data in the related websites.Industrial big data is a very important component of industrial internet.The main resources of the industrial big data were analyzed with introduction of the industrial internet.Then the relationships among “industrial big data,enterprise digitalization and industrial internet” were discussed.The“multi-typed,high-throughput,and strong-correlated” characteristics of industrial big data were presented.A typical industrial big data application scenario with “cross-time-scale,cross-supply-chain,and cross-organization” was shown.Furthermore,Industrial big data software architecture was discussed and a real-life application case was shown. 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 （1895）   HTML （300）    PDF（pc） （1542KB）（2231）       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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Technical challenges in applying zero-knowledge proof to blockchain Kang LI, Yi SUN, Jun ZHANG, Jun LI, Jihua ZHOU, Zhongcheng LI Big Data Research    2018, 4 (1): 57-65.   DOI: 10.11959/j.issn.2096-0271.2018006 Abstract （1843）   HTML （196）    PDF（pc） （1209KB）（1969）       Knowledge map    Save Blockchain is a peer-to-peer distributed ledger technology based on cryptography,However,open and transparent blockchain ledger,combined with sociological mining,data mining and other statistical methods,brings a major threat to user’s privacy.Therefore,privacy protection becomes a hot issue on the blockchain technology research.The existing privacy protection schemes were summarized,especially focusing on the zero-knowledge proof techniques.The technical challenges in applying zero-knowledge proof to blockchain privacy protection schemes were expounded and analyzed,and position solutions to these challenges were given. 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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Application of blockchain in data circulation Shu YAN, Sude QING, Kai WEI Big Data Research    2018, 4 (1): 3-12.   DOI: 10.11959/j.issn.2096-0271.2018001 Abstract （1781）   HTML （141）    PDF（pc） （1235KB）（1619）       Knowledge map    Save Circulation of big data is a key link in the value creation of data,but there are numerous problems to be solved.Blockchain is a distributed ledger technology,which can be applied in data circulation by its decentralizing and immutable property.The main idea of authorized deposit certificate,data traceability and smart contract using blockchain was discussed,and the overall architecture of data circulation by blockchain was proposed.Finally,some applications and other data security solutions were introduced. Table and Figures | Reference | 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）（1950）       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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Privacy protection mechanism for blockchain transaction data Liehuang ZHU, Hui DONG, Meng SHEN Big Data Research    2018, 4 (1): 46-56.   DOI: 10.11959/j.issn.2096-0271.2018005 Abstract （1755）   HTML （134）    PDF（pc） （1160KB）（2419）       Knowledge map    Save Blockchain technology is a distributed data storage technology that is de-centralized,de-trusted,open and transparent.It can reduce the cost of trust and realize safe and reliable data interaction.However,attackers can easily obtain the transaction data stored in the public ledger,and may extract transaction rules and other privacy information from this data by applying big data analysis techniques.Firstly,the thread of attack of data analysis on blockchain transaction data was analyzed,and the attack methods based on data analysis were described.Then the privacy protection mechanism of transaction data which was represented by mixing mechanism was introduced,the basic principle of various mixing methods was described in brief,and the advantages and disadvantages of different mixing approaches for the problem of whether a central node was needed in the process of mixing were analyzed.In the end,the limitation of the existing technologies and envision the future directions on this topic was discussed. Table and Figures | Reference | Related Articles | Metrics

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Deconstructing the EU General Data Protection Regulation Rong WANG Big Data Research    2016, 2 (4): 93-101.   DOI: 10.11959/j.issn.2096-0271.2016045 Abstract （1717）   HTML （264）    PDF（pc） （958KB）（4716）       Knowledge map    Save The EU General Data Protection Regulation (GDPR) has taken effect on May 25,2018.In order to catch up with the new trend of digital era,companies from all over the world are actively preparing for the related compliance work.The major changes of this new regulation were demonstrated comprehensively.It will provide a reference for companies and new prospective for China’s data protection policy making in big data era. Reference | Related Articles | Metrics

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Model and construction method of the ontology of knowledge graph of smart city Genlin ZANG, Yaqiang WANG, Qingrong WU, Chunli ZHAN, Yi LI Big Data Research    2020, 6 (2): 96-106.   DOI: 10.11959/j.issn.2096-0271.2020017 Abstract （1583）   HTML （358）    PDF（pc） （2316KB）（1795）       Knowledge map    Save Specific to problems such as insufficient data resource sharing and difficulty in implementing artificial intelligence applications in the current construction process of smart cities,based on resource description framework of knowledge graph,ontology knowledge system carrier,and digital twins,a knowledge graph model of smart city with data of people as the core was proposed,and a construction method of ontology and sub-ontology in multi-domain knowledge graph supporting the model was also proposed.The idea of “sky,earth and people” model was innovatively proposed,which will play a positive role in how the data of smart city serve urban residents,how to implement artificial intelligence algorithm models and smart city applications. Table and Figures | Reference | Related Articles | Metrics

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Optimizing distributed systems with remote direct memory access Xingda WEI, Rong CHEN, Haibo CHEN Big Data Research    2018, 4 (4): 3-14.   DOI: 10.11959/j.issn.2096-0271.2018036 Abstract （1513）   HTML （89）    PDF（pc） （1687KB）（2190）       Knowledge map    Save Fast network devices with RDMA support have been price-compatible with traditional network primitives such as Ethernet,and it’s now widely deployed in modern data centers.RDMA can be used in two ways.Firstly,it can optimize the messaging primitive in distributed applications.The second way is to redesign the applications with RDMA’s onesided features.One-sided features provide high CPU utilizations and high network performance,but the system should be redesigned.The research progress of RDMA was introduced.An overview on the research efforts on using RDMA for distributed systems was presented.The works on how to use RDMA to redesign systems and the works on how to better leverage RDMA were included.The future research directions were also put forward. Table and Figures | Reference | Related Articles | Metrics

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Smoke index:big data technologies monitor Internet financial risks Chonggang LI, Huiquan XU Big Data Research    2018, 4 (4): 76-84.   DOI: 10.11959/j.issn.2096-0271.2018042 Abstract （1510）   HTML （83）    PDF（pc） （1110KB）（1480）       Knowledge map    Save Based on the big data concept and techniques in the financial regulation,smoke index was taken as the core technology to the Internet financial risk monitoring system,and the real-time monitoring and integration of multi-heterogeneous risk information were carried out and a set of “people”,“capital” and “business” risk assessment indexes by using technologies such as big data,cloud computing and artificial intelligence were built.The means and efficiency of routine supervision of Internet financial risks were enhanced and optimized,and factual basis and data reference for the regulatory agencies’ decision-making was provided. Table and Figures | Reference | Related Articles | Metrics

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An initial exploration on framework of data assetization Yazhen YE, Guohua LIU, Yangyong ZHU Big Data Research    2020, 6 (3): 3-12.   DOI: 10.11959/j.issn.2096-0271.2020019 Abstract （1507）   HTML （404）    PDF（pc） （1076KB）（1569）       Knowledge map    Save As the digital economy develops,data as one of the key elements of the digital economy has been widely recognized as a new type of assets.However,various types of data cannot be treated as assets.Therefore,the criteria of data assets and the transformation from datasets to data assets are critical questions for data industry and the actors involved in data economy to solve.The features and requirements of data assets were discussed,and a basic framework for data assetization based on features of data assets was proposed,including five phases of registration of rights concerning data resources,data value confirmation and quality control,data box building and storage,asset pricing and evaluation,and data asset depreciation and appreciation management.A plausible path to data resource assetization was provided. Table and Figures | Reference | Supplementary Material | Related Articles | Metrics

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Study on big data governance standard system Hong DAI, Qun ZHANG, Zhuo YIN Big Data Research    2019, 5 (3): 47-54.   DOI: 10.11959/j.issn.2096-0271.2019023 Abstract （1459）   HTML （236）    PDF（pc） （1286KB）（1257）       Knowledge map    Save As big data has gradually stepped from the concept introduction period to the new stage of deepening pragmatic application,big data governance has become a new hot spot in the big data industry ecosystem.Its development requires the foundation of standard system construction and the support of standardization.The problems faced by China’s big data governance standardization were sorted out,the concepts and definitions related to big data governance were clarified,the big data governance standard system framework was proposed,and suggestions for the future standardization work were given,which can help the industry to build a new big data standard system which covering big data governance,and provide standardized support for China’s new stage of the development of big data technology and industrial. Table and Figures | Reference | Related Articles | Metrics

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Edge intelligence:state-of-the-art and expectations Kenli LI, Chubo LIU Big Data Research    2019, 5 (3): 69-75.   DOI: 10.11959/j.issn.2096-0271.2019025 Abstract （1450）   HTML （275）    PDF（pc） （1110KB）（1362）       Knowledge map    Save Edge intelligence (EI,which merges artificial intelligence (AI) into edge computing and deploys AI methods on edge devices) is regarded as a very efficient measure to provide faster and better intelligent services,having been successfully applied to various fields.However,current EI faces great difficulties.Firstly,a brief introduction to EI was given,and then,three challenges in EI were summarized.Finally,current five research directions for solving the EI challenges were outlined.The paper was expected to provide a better understanding for people who want to know EI,and help for researchers who study EI to have an overall direction guideline. Table and Figures | Reference | Related Articles | Metrics